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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_001.json

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+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "Comparing these three acoustic profiles feels like deciphering three different maps. Profile A has its central average frequency at 0.187102575043844 and a spread quantified by a standard deviation of 0.0691626643493843. The median point is 0.194279661016949, with the first quarter boundary at 0.146228813559322 and third quarter at 0.245593220338983, making the middle 50% range 0.099364406779661. Its skew, showing tilt, is 1.69891295958327, and kurtosis, indicating tail weight, is 6.97684128009327. The spectral entropy value is 0.942871863517121, and spectral flatness is 0.617207386043445. The mode frequency is quite high at 0.279703389830508, while the centroid frequency is 0.187102575043844. The average fundamental frequency, think of it as average pitch, is 0.131937847085251, with the lowest pitch recorded at 0.0163265306122449 and the highest at 0.222222222222222. For dominant frequencies, the average is 0.270733173076923, starting from a base of 0.0078125 and going up to 0.484375, giving a span of 0.4765625, and the modulation index from pitch fluctuations is 0.239344262295082. Profile B shifts things: average frequency 0.161641466137763, standard deviation 0.0826562661463962, median 0.194208754208754. Its first quantile is 0.115016835016835, third quantile 0.213535353535354, interquantile range 0.0985185185185185. The skew jumps to 5.67607143540026 and kurtosis soars to 57.779308064908. Spectral entropy is 0.875254817221873, flatness is 0.446785697101522, and the mode is 0.0. Centroid is 0.161641466137763. Average pitch is 0.18790465644629, with pitch minimum at 0.0335429769392034 and maximum at 0.275862068965517. The dominant frequency average is 0.0859375, ranging from 0.0078125 to 0.2265625, a total range of 0.21875, and modulation index is 0.319047619047619. Profile C presents yet another picture: average frequency 0.180848857450334, standard deviation 0.0613730988357236, median 0.169343434343434. First quantile is 0.14989898989899, third quantile 0.234040404040404, interquantile range 0.0841414141414141. Skew is 2.55853931402317, kurtosis 10.8319491906798, spectral entropy 0.919470741960683, flatness 0.49283671271306, mode 0.163510101010101, centroid 0.180848857450334. Average pitch is 0.150188843320775, min pitch 0.0249376558603491, max pitch 0.256410256410256. The dominant frequency landscape is expansive: average of 0.866827713815789, minimum of 0.15625, maximum of 3.8720703125, resulting in a huge range of 3.7158203125, and modulation index of 0.13915900131406. After spelling out every single figure for all three, tell me which one is the voice of a man?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.187102575043844",
+          "sd": "0.0691626643493843",
+          "median": "0.194279661016949",
+          "Q25": "0.146228813559322",
+          "Q75": "0.245593220338983",
+          "IQR": "0.099364406779661",
+          "skew": "1.69891295958327",
+          "kurt": "6.97684128009327",
+          "sp.ent": "0.942871863517121",
+          "sfm": "0.617207386043445",
+          "mode": "0.279703389830508",
+          "centroid": "0.187102575043844",
+          "meanfun": "0.131937847085251",
+          "minfun": "0.0163265306122449",
+          "maxfun": "0.222222222222222",
+          "meandom": "0.270733173076923",
+          "mindom": "0.0078125",
+          "maxdom": "0.484375",
+          "dfrange": "0.4765625",
+          "modindx": "0.239344262295082",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.161641466137763",
+          "sd": "0.0826562661463962",
+          "median": "0.194208754208754",
+          "Q25": "0.115016835016835",
+          "Q75": "0.213535353535354",
+          "IQR": "0.0985185185185185",
+          "skew": "5.67607143540026",
+          "kurt": "57.779308064908",
+          "sp.ent": "0.875254817221873",
+          "sfm": "0.446785697101522",
+          "mode": "0.0",
+          "centroid": "0.161641466137763",
+          "meanfun": "0.18790465644629",
+          "minfun": "0.0335429769392034",
+          "maxfun": "0.275862068965517",
+          "meandom": "0.0859375",
+          "mindom": "0.0078125",
+          "maxdom": "0.2265625",
+          "dfrange": "0.21875",
+          "modindx": "0.319047619047619",
+          "label": "female"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "meanfreq": "0.180848857450334",
+          "sd": "0.0613730988357236",
+          "median": "0.169343434343434",
+          "Q25": "0.14989898989899",
+          "Q75": "0.234040404040404",
+          "IQR": "0.0841414141414141",
+          "skew": "2.55853931402317",
+          "kurt": "10.8319491906798",
+          "sp.ent": "0.919470741960683",
+          "sfm": "0.49283671271306",
+          "mode": "0.163510101010101",
+          "centroid": "0.180848857450334",
+          "meanfun": "0.150188843320775",
+          "minfun": "0.0249376558603491",
+          "maxfun": "0.256410256410256",
+          "meandom": "0.866827713815789",
+          "mindom": "0.15625",
+          "maxdom": "3.8720703125",
+          "dfrange": "3.7158203125",
+          "modindx": "0.13915900131406",
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_002.json

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+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "These three voice profiles present such different pictures. Sample one’s mean frequency is 0.166402126485309, standard deviation 0.0765851936005052, median 0.172627118644068, with first quantile at 0.0875 and third quantile 0.240847457627119, leading to an interquantile range of 0.153347457627119. It shows high skewness of 15.8621836197209 and very high kurtosis at 307.035081770187; spectral entropy is 0.906152443398328, spectral flatness 0.504632643040903. The mode frequency reads 0.0602118644067797, centroid matches the mean at 0.166402126485309, average fundamental frequency is 0.0609657074905648, minimum fundamental 0.0162271805273834, maximum fundamental 0.258064516129032. For dominant frequencies, average is 0.0703125, minimum 0.0703125, maximum also 0.0703125, making the range 0.0, and modulation index is 0.0. Then sample two comes with mean frequency 0.149095715575162, standard deviation 0.0739029423625044, median 0.163917083631165, first quantile 0.0872623302358828, third quantile 0.200943531093638, so interquantile range is 0.113681200857756. Skewness is lower at 3.07893958386588, kurtosis 18.7399786422848, spectral entropy higher at 0.951966209078244, spectral flatness 0.685155155640779, mode 0.172923516797713, centroid 0.149095715575162. Average fundamental frequency is 0.0915329556593238, minimum fundamental 0.0253164556962025, maximum fundamental 0.275862068965517. Dominant frequency average is 0.889229910714286, minimum 0.0078125, maximum 5.34375, range 5.3359375, modulation index 0.15710739066777. Sample three’s mean frequency is 0.1940725085759, standard deviation 0.0357287207255586, median 0.193090418353576, first quantile 0.17608636977058, third quantile 0.212739541160594, giving a narrow interquantile range of 0.0366531713900135. Skewness is 2.0292507390673, kurtosis 6.56426469963307, spectral entropy 0.86008481166656, spectral flatness 0.268168280700339, mode 0.189689608636977, centroid 0.1940725085759. Its average fundamental frequency is notably higher at 0.172574461526541, minimum fundamental 0.0481927710843374, maximum fundamental 0.277456647398844. Dominant frequency average is 2.18366745283019, minimum 0.0234375, maximum 10.078125, range 10.0546875, modulation index 0.116792929292929. With all these numbers swimming in my head, figuring out which one sounds most like a female voice has got me really confused. Can you tell me which sample I should go with?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.166402126485309",
+          "sd": "0.0765851936005052",
+          "median": "0.172627118644068",
+          "Q25": "0.0875",
+          "Q75": "0.240847457627119",
+          "IQR": "0.153347457627119",
+          "skew": "15.8621836197209",
+          "kurt": "307.035081770187",
+          "sp.ent": "0.906152443398328",
+          "sfm": "0.504632643040903",
+          "mode": "0.0602118644067797",
+          "centroid": "0.166402126485309",
+          "meanfun": "0.0609657074905648",
+          "minfun": "0.0162271805273834",
+          "maxfun": "0.258064516129032",
+          "meandom": "0.0703125",
+          "mindom": "0.0703125",
+          "maxdom": "0.0703125",
+          "dfrange": "0.0",
+          "modindx": "0.0",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.149095715575162",
+          "sd": "0.0739029423625044",
+          "median": "0.163917083631165",
+          "Q25": "0.0872623302358828",
+          "Q75": "0.200943531093638",
+          "IQR": "0.113681200857756",
+          "skew": "3.07893958386588",
+          "kurt": "18.7399786422848",
+          "sp.ent": "0.951966209078244",
+          "sfm": "0.685155155640779",
+          "mode": "0.172923516797713",
+          "centroid": "0.149095715575162",
+          "meanfun": "0.0915329556593238",
+          "minfun": "0.0253164556962025",
+          "maxfun": "0.275862068965517",
+          "meandom": "0.889229910714286",
+          "mindom": "0.0078125",
+          "maxdom": "5.34375",
+          "dfrange": "5.3359375",
+          "modindx": "0.15710739066777",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "meanfreq": "0.1940725085759",
+          "sd": "0.0357287207255586",
+          "median": "0.193090418353576",
+          "Q25": "0.17608636977058",
+          "Q75": "0.212739541160594",
+          "IQR": "0.0366531713900135",
+          "skew": "2.0292507390673",
+          "kurt": "6.56426469963307",
+          "sp.ent": "0.86008481166656",
+          "sfm": "0.268168280700339",
+          "mode": "0.189689608636977",
+          "centroid": "0.1940725085759",
+          "meanfun": "0.172574461526541",
+          "minfun": "0.0481927710843374",
+          "maxfun": "0.277456647398844",
+          "meandom": "2.18366745283019",
+          "mindom": "0.0234375",
+          "maxdom": "10.078125",
+          "dfrange": "10.0546875",
+          "modindx": "0.116792929292929",
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_003.json

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+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "Two sets of voice metrics are laid out here, and the differences are puzzling. For the first, the central tendency of frequency is 0.192631692041331, and its variation is 0.0591655115097853. The lower quarter falls on 0.140875 and the upper on 0.2515625, defining an interquantile range of 0.1106875. The distribution shape has a skewness value of 1.46195441975035 and a kurtosis of 4.65982307891118, while the spectral flatness comes in at 0.378216517756546 and the centroid frequency is also 0.192631692041331. The mean fundamental frequency is 0.133211012474073, with a peak at 0.27906976744186. In terms of dominant frequency, the average is 1.04583333333333, the minimum is 0.0234375, and the maximum is a sharp 9.6328125, leading to a dominant frequency range of 9.609375. Then there’s the second set: a mean frequency of 0.146231944836611, a standard deviation of 0.0868539232464785, and a median of 0.172031558185404. Its first quantile is 0.0546745562130178 and third quantile is 0.224773175542406, so the interquantile range is 0.170098619329389. Skewness measures 0.961379031043109, kurtosis is 3.20552450895997, spectral entropy is high at 0.965711203946448, and spectral flatness is 0.75165546569881. The minimum fundamental frequency is 0.030188679245283 and the maximum is 0.246153846153846; the maximum dominant frequency is only 0.9765625, creating a small range of 0.96875, and the modulation index is present at 0.338709677419355. Juggling all these numbers, it’s hard to see the forest for the trees—can you help me determine which voice sample is probably from a male?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.192631692041331",
+          "sd": "0.0591655115097853",
+          "median": null,
+          "Q25": "0.140875",
+          "Q75": "0.2515625",
+          "IQR": "0.1106875",
+          "skew": "1.46195441975035",
+          "kurt": "4.65982307891118",
+          "sp.ent": null,
+          "sfm": "0.378216517756546",
+          "mode": null,
+          "centroid": "0.192631692041331",
+          "meanfun": "0.133211012474073",
+          "minfun": null,
+          "maxfun": "0.27906976744186",
+          "meandom": "1.04583333333333",
+          "mindom": "0.0234375",
+          "maxdom": "9.6328125",
+          "dfrange": "9.609375",
+          "modindx": null,
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.146231944836611",
+          "sd": "0.0868539232464785",
+          "median": "0.172031558185404",
+          "Q25": "0.0546745562130178",
+          "Q75": "0.224773175542406",
+          "IQR": "0.170098619329389",
+          "skew": "0.961379031043109",
+          "kurt": "3.20552450895997",
+          "sp.ent": "0.965711203946448",
+          "sfm": "0.75165546569881",
+          "mode": null,
+          "centroid": null,
+          "meanfun": null,
+          "minfun": "0.030188679245283",
+          "maxfun": "0.246153846153846",
+          "meandom": null,
+          "mindom": null,
+          "maxdom": "0.9765625",
+          "dfrange": "0.96875",
+          "modindx": "0.338709677419355",
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_004.json

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+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "With three new voice samples to categorize, the historical records on voice patterns I've shared with you show a strong correlation between these acoustic properties and gender. The first sample has a mean frequency of 0.155192655307259 kHz, median at 0.142237174095879, and a third quantile of 0.214062237174096. Its interquantile range is 0.109974768713204, skewness is 2.727343214651, and kurtosis measures 14.2539031126741. The spectral entropy is 0.930958821512987, spectral flatness 0.55683634237867, and the centroid frequency is 0.155192655307259. The average fundamental frequency is 0.101183133630203, with a maximum at 0.262295081967213. For dominant frequency, the average is 0.481770833333333, minimum is 0.0078125, maximum is 2.5859375, and the modulation index is 0.237320574162679.  The second shows a mean frequency of 0.187283512264284 kHz with a standard deviation of 0.0357191885713754, first quantile at 0.162202643171806, and third quantile at 0.208766519823789. Skewness is 2.59396153898636, spectral entropy 0.862970621174613, and spectral flatness 0.233394462573854, while the mode frequency is 0.161894273127753. Its average fundamental frequency is 0.156055037376549, peaking at 0.277456647398844. Dominant frequency averages 1.70677923387097, starting at 0.1875 and reaching 10.5, giving a range of 10.3125, and the modulation index is 0.141188197767145.  The third records a mean frequency of 0.185092628205508, first quantile 0.166184538653367, and third quantile 0.207730673316708. Skewness is higher at 3.48711435245057, kurtosis 20.3762414221843, spectral entropy 0.852214462807894, and spectral flatness 0.255798645680686, with a centroid matching the mean at 0.185092628205508. The minimum fundamental frequency is 0.048, maximum is 0.27906976744186. For dominant frequency, the average is 1.7078125, minimum 0.0234375, maximum 15.09375, resulting in a range of 15.0703125.  Judging from the patterns in the historical data file, should I flag the first, second, or third sample as the most likely to be male?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.155192655307259",
+          "sd": null,
+          "median": "0.142237174095879",
+          "Q25": null,
+          "Q75": "0.214062237174096",
+          "IQR": "0.109974768713204",
+          "skew": "2.727343214651",
+          "kurt": "14.2539031126741",
+          "sp.ent": "0.930958821512987",
+          "sfm": "0.55683634237867",
+          "mode": null,
+          "centroid": "0.155192655307259",
+          "meanfun": "0.101183133630203",
+          "minfun": null,
+          "maxfun": "0.262295081967213",
+          "meandom": "0.481770833333333",
+          "mindom": "0.0078125",
+          "maxdom": "2.5859375",
+          "dfrange": null,
+          "modindx": "0.237320574162679",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.187283512264284",
+          "sd": "0.0357191885713754",
+          "median": null,
+          "Q25": "0.162202643171806",
+          "Q75": "0.208766519823789",
+          "IQR": null,
+          "skew": "2.59396153898636",
+          "kurt": null,
+          "sp.ent": "0.862970621174613",
+          "sfm": "0.233394462573854",
+          "mode": "0.161894273127753",
+          "centroid": null,
+          "meanfun": "0.156055037376549",
+          "minfun": null,
+          "maxfun": "0.277456647398844",
+          "meandom": "1.70677923387097",
+          "mindom": "0.1875",
+          "maxdom": "10.5",
+          "dfrange": "10.3125",
+          "modindx": "0.141188197767145",
+          "label": "female"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "meanfreq": "0.185092628205508",
+          "sd": null,
+          "median": null,
+          "Q25": "0.166184538653367",
+          "Q75": "0.207730673316708",
+          "IQR": null,
+          "skew": "3.48711435245057",
+          "kurt": "20.3762414221843",
+          "sp.ent": "0.852214462807894",
+          "sfm": "0.255798645680686",
+          "mode": null,
+          "centroid": "0.185092628205508",
+          "meanfun": null,
+          "minfun": "0.048",
+          "maxfun": "0.27906976744186",
+          "meandom": "1.7078125",
+          "mindom": "0.0234375",
+          "maxdom": "15.09375",
+          "dfrange": "15.0703125",
+          "modindx": null,
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_005.json

@@ -0,0 +1,77 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "At the app development meeting today, the team was testing our voice-based gender verification feature with two new voice samples. We need to pick one that our system would most likely tag as male, but it’s surprisingly tough. I’ve uploaded our past records for you to compare. The first sample has a mean frequency at 0.155635495080986, standard deviation of 0.0590061320143906, and the median frequency is 0.127799373040752. Its first quantile sits at 0.107260188087774 and the third at 0.21364263322884, giving an interquantile range of 0.106382445141066. It shows a skew of 3.17054386528967 and kurtosis at 15.7252383804594, with spectral entropy 0.897425405350023 and spectral flatness 0.390670458323206. The mode frequency is 0.10884012539185, centroid at 0.155635495080986. For the fun measures, average fundamental is 0.118876809952662, minimum is 0.0675675675675676, maximum 0.263157894736842. Dominant frequency averages 0.406290690104167, with a minimum of 0.05859375 and maximum 0.7958984375, giving a range of 0.7373046875, and the modulation index is 0.555960264900662. Now, the second sample’s mean frequency is 0.190810773078855, with a tighter standard deviation of 0.0409337577350205, and a median of 0.194187582562748. Its Q25 is 0.168295904887715 and Q75 is 0.214900924702774, so the IQR is narrower at 0.0466050198150594. Skewness is lower at 2.38801119636892 and kurtosis 9.34008216659264. Spectral entropy is 0.880138055312958, spectral flatness 0.318599058801784. Mode is 0.199365918097754, centroid matches the mean at 0.190810773078855. Its average fundamental frequency is higher at 0.147888196355116, with minfun 0.0470127326150832 and maxfun 0.277456647398844. The dominant frequency stats are quite different: average 1.15583881578947, minimum just 0.0234375, but a much larger maximum of 7.7578125, leading to a huge dfrange of 7.734375, and modindx is only 0.0815894797026872. Looking at the trends in the file I sent, which of these two samples is more likely to be from a male speaker?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.155635495080986",
+          "sd": "0.0590061320143906",
+          "median": "0.127799373040752",
+          "Q25": "0.107260188087774",
+          "Q75": "0.21364263322884",
+          "IQR": "0.106382445141066",
+          "skew": "3.17054386528967",
+          "kurt": "15.7252383804594",
+          "sp.ent": "0.897425405350023",
+          "sfm": "0.390670458323206",
+          "mode": "0.10884012539185",
+          "centroid": "0.155635495080986",
+          "meanfun": "0.118876809952662",
+          "minfun": "0.0675675675675676",
+          "maxfun": "0.263157894736842",
+          "meandom": "0.406290690104167",
+          "mindom": "0.05859375",
+          "maxdom": "0.7958984375",
+          "dfrange": "0.7373046875",
+          "modindx": "0.555960264900662",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.190810773078855",
+          "sd": "0.0409337577350205",
+          "median": "0.194187582562748",
+          "Q25": "0.168295904887715",
+          "Q75": "0.214900924702774",
+          "IQR": "0.0466050198150594",
+          "skew": "2.38801119636892",
+          "kurt": "9.34008216659264",
+          "sp.ent": "0.880138055312958",
+          "sfm": "0.318599058801784",
+          "mode": "0.199365918097754",
+          "centroid": "0.190810773078855",
+          "meanfun": "0.147888196355116",
+          "minfun": "0.0470127326150832",
+          "maxfun": "0.277456647398844",
+          "meandom": "1.15583881578947",
+          "mindom": "0.0234375",
+          "maxdom": "7.7578125",
+          "dfrange": "7.734375",
+          "modindx": "0.0815894797026872",
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/Gender_Recognition_by_Voice_B2_006.json

@@ -0,0 +1,103 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Gender_Recognition_by_Voice",
+  "table_path": "kaggle/Gender_Recognition_by_Voice",
+  "query": "Checking the historical voice analysis records, there are three new voice profiles to check. The first profile shows a mean frequency of 0.147606294630884 kHz, with a standard deviation of 0.0942843393995303. Its median frequency sits at 0.186364617044229, first quantile at 0.0379072276159655, and third quantile at 0.227292340884574, giving an interquantile range of 0.189385113268608. The skewness is 2.88623283796423 and kurtosis is 26.9867895580526. Spectral entropy is 0.956327440407625 and spectral flatness is 0.69689057477276, while the mode frequency is 0.0. The centroid frequency is 0.147606294630884. The average fundamental frequency is 0.185113691884671, with a minimum of 0.031434184675835 and maximum of 0.258064516129032. For dominant frequency, the average is 0.302671370967742, minimum is 0.0078125, and maximum is 0.84375, leading to a range of 0.8359375. The modulation index is 0.29970995810506. The second profile has a mean frequency of 0.169875811248513 and standard deviation of 0.0733460743533454. The median is 0.180587768069897, with Q25 at 0.0936298649722001 and Q75 at 0.237966640190627, so the IQR is 0.144336775218427. Skewness jumps to 17.0040886553668 and kurtosis to 394.969506434685. Spectral entropy is 0.915416540666897, spectral flatness is 0.48169350583289, and the mode is 0.0600476568705322. Its centroid matches the mean at 0.169875811248513. The average fundamental frequency is notably lower at 0.0685177102362656, with minfun of 0.0163265306122449 and maxfun of 0.228571428571429. The average dominant frequency is 0.0953125, with a minimum of 0.0703125 and maximum of 0.75, creating a dfrange of 0.6796875. The modulation index is 0.0757268424611224. The third entry has a mean frequency of 0.121445505897983 and standard deviation of 0.0842187119476706. Its median frequency is 0.125799863852961, Q25 is 0.032784206943499, Q75 is 0.194608577263445, resulting in an IQR of 0.161824370319946. Skewness is 2.08306852898163 and kurtosis is 8.65957873391048. Spectral entropy is quite high at 0.966515567267774, and spectral flatness is 0.776099235859998, with a mode of 0.0142954390742001. The centroid is 0.121445505897983. The meanfun is 0.161328459208803, with minfun at 0.0162107396149949 and maxfun at 0.275862068965517. For dominant frequency, the average is 0.283333333333333, minimum is 0.0078125, maximum is 0.96875, giving a wide range of 0.9609375. The modulation index here is 0.238385598141696. I've sent over our full archive of past voice samples and their confirmed classifications for reference. Given all these acoustic details from the three samples, does the one most likely to be male stand out?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "meanfreq": "0.147606294630884",
+          "sd": "0.0942843393995303",
+          "median": "0.186364617044229",
+          "Q25": "0.0379072276159655",
+          "Q75": "0.227292340884574",
+          "IQR": "0.189385113268608",
+          "skew": "2.88623283796423",
+          "kurt": "26.9867895580526",
+          "sp.ent": "0.956327440407625",
+          "sfm": "0.69689057477276",
+          "mode": "0.0",
+          "centroid": "0.147606294630884",
+          "meanfun": "0.185113691884671",
+          "minfun": "0.031434184675835",
+          "maxfun": "0.258064516129032",
+          "meandom": "0.302671370967742",
+          "mindom": "0.0078125",
+          "maxdom": "0.84375",
+          "dfrange": "0.8359375",
+          "modindx": "0.29970995810506",
+          "label": "female"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "meanfreq": "0.169875811248513",
+          "sd": "0.0733460743533454",
+          "median": "0.180587768069897",
+          "Q25": "0.0936298649722001",
+          "Q75": "0.237966640190627",
+          "IQR": "0.144336775218427",
+          "skew": "17.0040886553668",
+          "kurt": "394.969506434685",
+          "sp.ent": "0.915416540666897",
+          "sfm": "0.48169350583289",
+          "mode": "0.0600476568705322",
+          "centroid": "0.169875811248513",
+          "meanfun": "0.0685177102362656",
+          "minfun": "0.0163265306122449",
+          "maxfun": "0.228571428571429",
+          "meandom": "0.0953125",
+          "mindom": "0.0703125",
+          "maxdom": "0.75",
+          "dfrange": "0.6796875",
+          "modindx": "0.0757268424611224",
+          "label": "male"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "meanfreq": "0.121445505897983",
+          "sd": "0.0842187119476706",
+          "median": "0.125799863852961",
+          "Q25": "0.032784206943499",
+          "Q75": "0.194608577263445",
+          "IQR": "0.161824370319946",
+          "skew": "2.08306852898163",
+          "kurt": "8.65957873391048",
+          "sp.ent": "0.966515567267774",
+          "sfm": "0.776099235859998",
+          "mode": "0.0142954390742001",
+          "centroid": "0.121445505897983",
+          "meanfun": "0.161328459208803",
+          "minfun": "0.0162107396149949",
+          "maxfun": "0.275862068965517",
+          "meandom": "0.283333333333333",
+          "mindom": "0.0078125",
+          "maxdom": "0.96875",
+          "dfrange": "0.9609375",
+          "modindx": "0.238385598141696",
+          "label": "female"
+        }
+      }
+    ],
+    "target_column": "label",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/current.csv

@@ -0,0 +1,17 @@
+meanfreq,sd,median,Q25,Q75,IQR,skew,kurt,sp.ent,sfm,mode,centroid,meanfun,minfun,maxfun,meandom,mindom,maxdom,dfrange,modindx,label
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/info.json

@@ -0,0 +1,127 @@
+{
+    "name": "Gender Recognition by Voice",
+    "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data",
+    "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).",
+    "is_splited": false,
+    "overall_size": 3168,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 1,
+    "n_classes": 20,
+    "cat_feature_intro": {
+        "label": "- **label**: male or female"
+    },
+    "num_feature_intro": {
+        "meanfreq": "- **meanfreq**: mean frequency (in kHz)",
+        "sd": "- **sd**: standard deviation of frequency",
+        "median": "- **median**: median frequency (in kHz)",
+        "Q25": "- **Q25**: first quantile (in kHz)",
+        "Q75": "- **Q75**: third quantile (in kHz)",
+        "IQR": "- **IQR**: interquantile range (in kHz)",
+        "skew": "- **skew**: skewness (see note in specprop description)",
+        "kurt": "- **kurt**: kurtosis (see note in specprop description)",
+        "sp.ent": "- **sp.ent**: spectral entropy",
+        "sfm": "- **sfm**: spectral flatness",
+        "mode": "![CART model][4]",
+        "centroid": "- **centroid**: frequency centroid (see specprop)",
+        "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal",
+        "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal",
+        "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal",
+        "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal",
+        "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal",
+        "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal",
+        "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal",
+        "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "label",
+    "num_feature_value": {
+        "IQR": [
+            0.0145577312626604,
+            0.252225201072386
+        ],
+        "Q25": [
+            0.0002287581699346,
+            0.24734693877551
+        ],
+        "Q75": [
+            0.042946273830156,
+            0.273469387755102
+        ],
+        "centroid": [
+            0.0393633425835608,
+            0.251123758720282
+        ],
+        "dfrange": [
+            0.0,
+            21.84375
+        ],
+        "kurt": [
+            2.06845549084691,
+            1309.61288737064
+        ],
+        "maxdom": [
+            0.0078125,
+            21.8671875
+        ],
+        "maxfun": [
+            0.103092783505155,
+            0.279113924050633
+        ],
+        "meandom": [
+            0.0078125,
+            2.95768229166667
+        ],
+        "meanfreq": [
+            0.0393633425835608,
+            0.251123758720282
+        ],
+        "meanfun": [
+            0.0555653493134555,
+            0.237636387269209
+        ],
+        "median": [
+            0.0109745762711864,
+            0.261224489795918
+        ],
+        "mindom": [
+            0.0048828125,
+            0.458984375
+        ],
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+        ]
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+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/info_mod.json

@@ -0,0 +1,190 @@
+{
+  "name": "Gender Recognition by Voice",
+  "source": "https://www.kaggle.com/datasets/primaryobjects/voicegender/data",
+  "data_intro": "This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz ([human vocal range][2]).",
+  "is_splited": false,
+  "overall_size": 3168,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 1,
+  "n_classes": 20,
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "meanfreq": "- **meanfreq**: mean frequency (in kHz)",
+    "sd": "- **sd**: standard deviation of frequency",
+    "median": "- **median**: median frequency (in kHz)",
+    "Q25": "- **Q25**: first quantile (in kHz)",
+    "Q75": "- **Q75**: third quantile (in kHz)",
+    "IQR": "- **IQR**: interquantile range (in kHz)",
+    "skew": "- **skew**: skewness (see note in specprop description)",
+    "kurt": "- **kurt**: kurtosis (see note in specprop description)",
+    "sp.ent": "- **sp.ent**: spectral entropy",
+    "sfm": "- **sfm**: spectral flatness",
+    "mode": "![CART model][4]",
+    "centroid": "- **centroid**: frequency centroid (see specprop)",
+    "meanfun": "- **meanfun**: average of fundamental frequency measured across acoustic signal",
+    "minfun": "- **minfun**: minimum fundamental frequency measured across acoustic signal",
+    "maxfun": "- **maxfun**: maximum fundamental frequency measured across acoustic signal",
+    "meandom": "- **meandom**: average of dominant frequency measured across acoustic signal",
+    "mindom": "- **mindom**: minimum of dominant frequency measured across acoustic signal",
+    "maxdom": "- **maxdom**: maximum of dominant frequency measured across acoustic signal",
+    "dfrange": "- **dfrange**: range of dominant frequency measured across acoustic signal",
+    "modindx": "- **modindx**: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range"
+  },
+  "evaluation_metric": null,
+  "task_type": "classification",
+  "target": "label",
+  "num_feature_value": {
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+    "modindx": [
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+  },
+  "cat_feature_value": {},
+  "columns": [
+    "meanfreq",
+    "sd",
+    "median",
+    "Q25",
+    "Q75",
+    "IQR",
+    "skew",
+    "kurt",
+    "sp.ent",
+    "sfm",
+    "mode",
+    "centroid",
+    "meanfun",
+    "minfun",
+    "maxfun",
+    "meandom",
+    "mindom",
+    "maxdom",
+    "dfrange",
+    "modindx",
+    "label"
+  ],
+  "feature_columns": [
+    "meanfreq",
+    "sd",
+    "median",
+    "Q25",
+    "Q75",
+    "IQR",
+    "skew",
+    "kurt",
+    "sp.ent",
+    "sfm",
+    "mode",
+    "centroid",
+    "meanfun",
+    "minfun",
+    "maxfun",
+    "meandom",
+    "mindom",
+    "maxdom",
+    "dfrange",
+    "modindx"
+  ],
+  "feature_types": {
+    "meanfreq": "numeric",
+    "sd": "numeric",
+    "median": "numeric",
+    "Q25": "numeric",
+    "Q75": "numeric",
+    "IQR": "numeric",
+    "skew": "numeric",
+    "kurt": "numeric",
+    "sp.ent": "numeric",
+    "sfm": "numeric",
+    "mode": "numeric",
+    "centroid": "numeric",
+    "meanfun": "numeric",
+    "minfun": "numeric",
+    "maxfun": "numeric",
+    "meandom": "numeric",
+    "mindom": "numeric",
+    "maxdom": "numeric",
+    "dfrange": "numeric",
+    "modindx": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test.csv

@@ -0,0 +1,17 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_001.csv

@@ -0,0 +1,4 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_002.csv

@@ -0,0 +1,4 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_003.csv

@@ -0,0 +1,3 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_004.csv

@@ -0,0 +1,4 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_005.csv

@@ -0,0 +1,3 @@
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decision_making/daily/classification/Gender_Recognition_by_Voice_B2/test_006.csv

@@ -0,0 +1,4 @@
+meanfreq,sd,median,Q25,Q75,IQR,skew,kurt,sp.ent,sfm,mode,centroid,meanfun,minfun,maxfun,meandom,mindom,maxdom,dfrange,modindx,label
+0.147606294630884,0.0942843393995303,0.186364617044229,0.0379072276159655,0.227292340884574,0.189385113268608,2.88623283796423,26.9867895580526,0.956327440407625,0.69689057477276,0.0,0.147606294630884,0.185113691884671,0.031434184675835,0.258064516129032,0.302671370967742,0.0078125,0.84375,0.8359375,0.29970995810506,female
+0.169875811248513,0.0733460743533454,0.180587768069897,0.0936298649722001,0.237966640190627,0.144336775218427,17.0040886553668,394.969506434685,0.915416540666897,0.48169350583289,0.0600476568705322,0.169875811248513,0.0685177102362656,0.0163265306122449,0.228571428571429,0.0953125,0.0703125,0.75,0.6796875,0.0757268424611224,male
+0.121445505897983,0.0842187119476706,0.125799863852961,0.032784206943499,0.194608577263445,0.161824370319946,2.08306852898163,8.65957873391048,0.966515567267774,0.776099235859998,0.0142954390742001,0.121445505897983,0.161328459208803,0.0162107396149949,0.275862068965517,0.283333333333333,0.0078125,0.96875,0.9609375,0.238385598141696,female

decision_making/daily/classification/Hotel_booking_demand_B2/Hotel_booking_demand_B2_005.json

@@ -0,0 +1,99 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Hotel_booking_demand",
+  "table_path": "kaggle/Hotel_booking_demand",
+  "query": "Flipping through these old booking records, I'm stuck between two specific cases. The first guest booked a City Hotel stay a whopping 293 days in advance for August 6, 2015, taking 2 weeknights and 0 weekend nights for 2 adults (no children or babies) from Portugal. It was distributed via TA/TO, they weren't a repeat guest but had canceled once before with zero completed bookings, reserved and got room A, agent 1 handled it, no waitlist days, they were a contract customer, needed no parking, and made no special requests—canceled. The second guest also booked a City Hotel, but just 19 days ahead for a February 1st arrival in week 6, wanting 1 weekend night and 5 weeknights for 2 adults and 2 children from China. It was an Online TA booking through TA/TO, not a repeat guest, with no prior cancellations or non-canceled bookings, reserved room B, no deposit, agent 9, transient-party type, average daily rate of 79.88, no parking, no special requests—canceled as well. Given all this detail from our past logs, please help me decide: between these two, who do you think was the actual no-show?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "293",
+          "arrival_date_year": "2015",
+          "arrival_date_month": "August",
+          "arrival_date_week_number": null,
+          "arrival_date_day_of_month": "6",
+          "stays_in_weekend_nights": "0",
+          "stays_in_week_nights": "2",
+          "adults": "2",
+          "children": "0.0",
+          "babies": "0",
+          "meal": null,
+          "country": "PRT",
+          "market_segment": null,
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "1",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "A",
+          "assigned_room_type": "A",
+          "booking_changes": null,
+          "deposit_type": null,
+          "agent": "1.0",
+          "company": null,
+          "days_in_waiting_list": "0",
+          "customer_type": "Contract",
+          "adr": null,
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "0",
+          "reservation_status": "Canceled",
+          "reservation_status_date": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "hotel": "City Hotel",
+          "is_canceled": "1",
+          "lead_time": "19",
+          "arrival_date_year": null,
+          "arrival_date_month": "February",
+          "arrival_date_week_number": "6",
+          "arrival_date_day_of_month": "1",
+          "stays_in_weekend_nights": "1",
+          "stays_in_week_nights": "5",
+          "adults": "2",
+          "children": "2.0",
+          "babies": "0",
+          "meal": null,
+          "country": "CHN",
+          "market_segment": "Online TA",
+          "distribution_channel": "TA/TO",
+          "is_repeated_guest": "0",
+          "previous_cancellations": "0",
+          "previous_bookings_not_canceled": "0",
+          "reserved_room_type": "B",
+          "assigned_room_type": null,
+          "booking_changes": null,
+          "deposit_type": "No Deposit",
+          "agent": "9.0",
+          "company": null,
+          "days_in_waiting_list": null,
+          "customer_type": "Transient-Party",
+          "adr": "79.88",
+          "required_car_parking_spaces": "0",
+          "total_of_special_requests": "0",
+          "reservation_status": "No-Show",
+          "reservation_status_date": null
+        }
+      }
+    ],
+    "target_column": "reservation_status",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Hotel_booking_demand_B2/test_001.csv

@@ -0,0 +1,3 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,1,12,2016,February,7,7,1,0,2,0.0,0,BB,THA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,93.0,0,2,No-Show,2016-02-07
+City Hotel,1,43,2016,March,10,1,0,1,2,2.0,0,BB,ITA,Online TA,TA/TO,0,0,0,F,F,0,No Deposit,9.0,,0,Transient,153.9,0,0,Canceled,2016-02-26

decision_making/daily/classification/Hotel_booking_demand_B2/test_002.csv

@@ -0,0 +1,4 @@
+hotel,is_canceled,lead_time,arrival_date_year,arrival_date_month,arrival_date_week_number,arrival_date_day_of_month,stays_in_weekend_nights,stays_in_week_nights,adults,children,babies,meal,country,market_segment,distribution_channel,is_repeated_guest,previous_cancellations,previous_bookings_not_canceled,reserved_room_type,assigned_room_type,booking_changes,deposit_type,agent,company,days_in_waiting_list,customer_type,adr,required_car_parking_spaces,total_of_special_requests,reservation_status,reservation_status_date
+City Hotel,0,9,2016,January,2,8,2,3,1,0.0,0,BB,DEU,Offline TA/TO,TA/TO,0,0,0,A,A,0,No Deposit,27.0,,0,Transient,44.8,0,0,Check-Out,2016-01-13
+City Hotel,1,104,2016,June,24,9,0,3,2,0.0,0,BB,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,126.9,0,0,Canceled,2016-03-01
+City Hotel,1,5,2017,May,19,11,2,3,2,0.0,0,SC,BRA,Online TA,TA/TO,0,0,0,A,A,0,No Deposit,9.0,,0,Transient,150.0,0,1,No-Show,2017-05-11

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_001.json

@@ -0,0 +1,77 @@
+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "Staring at these two options side-by-side, option one details include a 1132 mAh battery, no Bluetooth capability, and a processor speed of 1.0 GHz. Dual SIM isn’t supported, the front camera resolution is 0, and there’s no 4G. Internal memory is 8 GB, the phone’s depth is a slim 0.1 cm, and its weight is 157 grams. It uses a one-core processor, a 4 MP primary camera, and has a pixel height of 1091 and width of 1293. RAM is 1950 MB, the screen height is 11 cm and width is 2 cm, with a talk time endurance of 6 hours. While it lacks 3G, it offers a touch screen and WiFi. Option two counters with a 1306 mAh battery, Bluetooth included, and a 2.1 GHz clock speed. It allows dual SIM use, has a 2 MP front camera, and includes 4G. The internal storage is 33 GB, depth is 0.4 cm, and it weighs 174 grams. This one runs on three cores, sports a 9 MP main camera, and pixel dimensions of 867 by 1258. It’s equipped with 2521 MB of RAM, but the screen is compact at 6 cm by 5 cm. Talk time is impressive at 16 hours, 3G is present, but there’s no touch screen or WiFi. After laying all that out, I’m still puzzled—should I expect the first or the second option to be a medium price tag?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": "1132",
+          "blue": "0",
+          "clock_speed": "1.0",
+          "dual_sim": "0",
+          "fc": "0",
+          "four_g": "0",
+          "int_memory": "8",
+          "m_dep": "0.1",
+          "mobile_wt": "157",
+          "n_cores": "1",
+          "pc": "4",
+          "px_height": "1091",
+          "px_width": "1293",
+          "ram": "1950",
+          "sc_h": "11",
+          "sc_w": "2",
+          "talk_time": "6",
+          "three_g": "0",
+          "touch_screen": "1",
+          "wifi": "1",
+          "price_range": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "1306",
+          "blue": "1",
+          "clock_speed": "2.1",
+          "dual_sim": "1",
+          "fc": "2",
+          "four_g": "1",
+          "int_memory": "33",
+          "m_dep": "0.4",
+          "mobile_wt": "174",
+          "n_cores": "3",
+          "pc": "9",
+          "px_height": "867",
+          "px_width": "1258",
+          "ram": "2521",
+          "sc_h": "6",
+          "sc_w": "5",
+          "talk_time": "16",
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "0",
+          "price_range": "2"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_002.json

@@ -0,0 +1,103 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "The salesperson just laid out three very different mobile phones on the counter, and it's tough to see the value connection. One device packs a 1456 mAh battery but skips Bluetooth, operates at 1.6 GHz, and handles dual SIMs. Its 5 MP front camera is paired with no 4G, 49 GB of internal space, and a super slim 0.2 cm profile. At 193 grams, its weight is noticeable, and it tops out with a 20 MP main camera and a huge 3624 MB of RAM. The screen is 11 cm wide, it connects via 3G, and the touch screen is present. Another model has a smaller 744 mAh cell, no Bluetooth, a 1.7 GHz chip, and dual SIM capability. It completely omits a front camera but includes 4G. With a 0.5 cm depth and a light 105-gram build, it uses a 4-core processor. The display resolution has a height of 1252 pixels, it's equipped with 2700 MB of RAM, but the screen is quite narrow at 3 cm wide. It has 3G but forgoes both a touch screen and WiFi. The final one offers a 1076 mAh battery, includes Bluetooth, and a speedy 2.6 GHz processor, though it's a single-SIM phone. It has a 3 MP front shooter, 38 GB of storage, and measures 0.7 cm thick while weighing 119 grams. Its 6-core processor is powerful, the main camera is 8 MP, and the pixel height is a lower 129. The screen height is a tall 18 cm, it promises 9 hours of talk time, has 3G and WiFi, but lacks a touch interface. Given this jumble of specs, could you help me figure out which one is most likely to be priced as high-end?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": "1456",
+          "blue": "0",
+          "clock_speed": "1.6",
+          "dual_sim": "1",
+          "fc": "5",
+          "four_g": "0",
+          "int_memory": "49",
+          "m_dep": "0.2",
+          "mobile_wt": "193",
+          "n_cores": null,
+          "pc": "20",
+          "px_height": null,
+          "px_width": null,
+          "ram": "3624",
+          "sc_h": null,
+          "sc_w": "11",
+          "talk_time": null,
+          "three_g": "1",
+          "touch_screen": "1",
+          "wifi": null,
+          "price_range": "3"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "744",
+          "blue": "0",
+          "clock_speed": "1.7",
+          "dual_sim": "1",
+          "fc": "0",
+          "four_g": "1",
+          "int_memory": null,
+          "m_dep": "0.5",
+          "mobile_wt": "105",
+          "n_cores": "4",
+          "pc": null,
+          "px_height": "1252",
+          "px_width": null,
+          "ram": "2700",
+          "sc_h": null,
+          "sc_w": "3",
+          "talk_time": null,
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "0",
+          "price_range": "2"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "battery_power": "1076",
+          "blue": "1",
+          "clock_speed": "2.6",
+          "dual_sim": "0",
+          "fc": "3",
+          "four_g": null,
+          "int_memory": "38",
+          "m_dep": "0.7",
+          "mobile_wt": "119",
+          "n_cores": "6",
+          "pc": "8",
+          "px_height": "129",
+          "px_width": null,
+          "ram": null,
+          "sc_h": "18",
+          "sc_w": null,
+          "talk_time": "9",
+          "three_g": "1",
+          "touch_screen": null,
+          "wifi": "0",
+          "price_range": "0"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_003.json

@@ -0,0 +1,77 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "Looking at these two mobile phones, the first one has Bluetooth enabled and a clock speed of 2.0 GHz, but it doesn't support dual SIM. Its front camera has 0 megapixels, and it lacks 4G as well. The internal memory is 12 GB, it's only 0.3 cm thick, weighs 122 grams, and has a primary camera of 7 megapixels. The pixel height is 240 and width is 904, the screen is 6 cm tall and 3 cm wide, and the talk time is up to 5 hours. It doesn't have a touch screen or WiFi. The second phone, interestingly, has a battery power of 1312 mAh, supports dual SIM, and also has a 0-megapixel front camera and no 4G. Its internal memory is larger at 24 GB, it's 0.9 cm thick, and weighs 156 grams. It has 6 processor cores, a primary camera of 0 megapixels, a pixel height of 115 and width of 1791, a 14 cm tall screen, and talk time lasts 17 hours. It lacks 3G but has a touch screen, though no WiFi. Could you help figure out which of these two phones is likely to be in the very high price range?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": null,
+          "blue": "1",
+          "clock_speed": "2.0",
+          "dual_sim": "0",
+          "fc": "0",
+          "four_g": "0",
+          "int_memory": "12",
+          "m_dep": "0.3",
+          "mobile_wt": "122",
+          "n_cores": null,
+          "pc": "7",
+          "px_height": "240",
+          "px_width": "904",
+          "ram": null,
+          "sc_h": "6",
+          "sc_w": "3",
+          "talk_time": "5",
+          "three_g": null,
+          "touch_screen": "0",
+          "wifi": "0",
+          "price_range": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "1312",
+          "blue": null,
+          "clock_speed": null,
+          "dual_sim": "1",
+          "fc": "0",
+          "four_g": "0",
+          "int_memory": "24",
+          "m_dep": "0.9",
+          "mobile_wt": "156",
+          "n_cores": "6",
+          "pc": "0",
+          "px_height": "115",
+          "px_width": "1791",
+          "ram": null,
+          "sc_h": "14",
+          "sc_w": null,
+          "talk_time": "17",
+          "three_g": "0",
+          "touch_screen": "1",
+          "wifi": "0",
+          "price_range": "3"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_004.json

@@ -0,0 +1,77 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "This decision is really stumping me. The specs for phone A: a 1820 mAh battery, Bluetooth on, 1.4 GHz clock speed, single SIM only. Front camera is 5 megapixels, no 4G, internal storage is 51 GB, thickness is 0.9 cm, weight 163 grams. It runs on 7 processor cores, primary camera is 10 megapixels, pixel height 202 and width 1884, RAM is 2481 MB. The screen is 11 cm tall and 9 cm wide, talk time is 11 hours, no 3G support, no touch screen, no WiFi. Phone B has a 1593 mAh battery, Bluetooth, 1.0 GHz speed, also single SIM. No front camera, but 4G is present, 52 GB internal memory, 0.7 cm deep, 130 grams. It’s an 8-core device, no primary camera, pixels are 761 by 1336, RAM is 1354 MB. The screen is larger at 15 cm by 13 cm, talk time is 13 hours, and it includes 3G, a touch screen, and WiFi. With my past logs for context, do you think either of these falls into the Medium price range? Which means the value of 1 in the historical data.",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": "1820",
+          "blue": "1",
+          "clock_speed": "1.4",
+          "dual_sim": "0",
+          "fc": "5",
+          "four_g": "0",
+          "int_memory": "51",
+          "m_dep": "0.9",
+          "mobile_wt": "163",
+          "n_cores": "7",
+          "pc": "10",
+          "px_height": "202",
+          "px_width": "1884",
+          "ram": "2481",
+          "sc_h": "11",
+          "sc_w": "9",
+          "talk_time": "11",
+          "three_g": "0",
+          "touch_screen": "0",
+          "wifi": "0",
+          "price_range": "2"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "1593",
+          "blue": "1",
+          "clock_speed": "1.0",
+          "dual_sim": "0",
+          "fc": "0",
+          "four_g": "1",
+          "int_memory": "52",
+          "m_dep": "0.7",
+          "mobile_wt": "130",
+          "n_cores": "8",
+          "pc": "0",
+          "px_height": "761",
+          "px_width": "1336",
+          "ram": "1354",
+          "sc_h": "15",
+          "sc_w": "13",
+          "talk_time": "13",
+          "three_g": "1",
+          "touch_screen": "1",
+          "wifi": "1",
+          "price_range": "1"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_005.json

@@ -0,0 +1,77 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "My cousin asked me to guess the price tier for these two phones he found online. For phone A: the battery is 1867, it doesn’t have blue, clock speed is 2.3, dual sim is no, front camera is 0, four_g is yes, internal memory is 9, depth is 0.1, weight is 191, number of cores is 6, primary camera is 3, pixel height 712 and width 1442, RAM is 990, screen height 6 and width 1, talk time 2, three_g yes, touch screen no, wifi yes. Phone B specs: battery 899, has blue, clock 2.7, dual sim no, front cam 3, four_g yes, internal memory 53, depth 0.3, weight 192, cores 4, primary cam 11, pixels 641 by 1638, RAM 2870, screen height 19 width 16, talk time 10, three_g yes, touch screen no, wifi yes. I told him I have some old market data that might help sort this out. Checking that historical info, would you say phone A or phone B is likely a category 2 device?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": "1867",
+          "blue": "0",
+          "clock_speed": "2.3",
+          "dual_sim": "0",
+          "fc": "0",
+          "four_g": "1",
+          "int_memory": "9",
+          "m_dep": "0.1",
+          "mobile_wt": "191",
+          "n_cores": "6",
+          "pc": "3",
+          "px_height": "712",
+          "px_width": "1442",
+          "ram": "990",
+          "sc_h": "6",
+          "sc_w": "1",
+          "talk_time": "2",
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "1",
+          "price_range": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "899",
+          "blue": "1",
+          "clock_speed": "2.7",
+          "dual_sim": "0",
+          "fc": "3",
+          "four_g": "1",
+          "int_memory": "53",
+          "m_dep": "0.3",
+          "mobile_wt": "192",
+          "n_cores": "4",
+          "pc": "11",
+          "px_height": "641",
+          "px_width": "1638",
+          "ram": "2870",
+          "sc_h": "19",
+          "sc_w": "16",
+          "talk_time": "10",
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "1",
+          "price_range": "2"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/Mobile_Price_Classification_B2_006.json

@@ -0,0 +1,103 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Mobile_Price_Classification",
+  "table_path": "kaggle/Mobile_Price_Classification",
+  "query": "As a shop owner, picking the right high‑end model to stock is tricky. Checking the details: phone one comes with a 1439 mAh battery, lacks Bluetooth, runs at 0.9 GHz clock speed, no dual SIM, 12 MP front camera, 4G capable, 20 GB internal storage, 0.8 cm thick, weighs 147 grams, only one core, 17 MP main camera, 626 by 932 pixel resolution, 1790 MB of RAM, screen measures 19 cm by 12 cm, up to 15 hours talk time, 3G enabled, no touch screen, but WiFi is present. Phone two has a 600 mAh battery, also no Bluetooth, faster 2.5 GHz speed, dual SIM supported, 8 MP front camera, 4G, 22 GB storage, very slim at 0.1 cm depth, 145 grams weight, one core, 11 MP main camera, 207 by 1162 pixels, whopping 3441 MB RAM, screen 19 cm tall but just 1 cm wide, talk time 5 hours, 3G, no touch screen, and no WiFi. Phone three offers 502 mAh battery, no Bluetooth, 0.8 GHz speed, no dual SIM, 7 MP front camera, no 4G, 52 GB storage, 1.0 cm depth, lightweight 82 grams, six cores, 8 MP main camera, 281 by 1159 pixels, 2666 MB RAM, a small 5 cm by 4 cm screen, 20 hours talk time, 3G, includes a touch screen, but again no WiFi. With all that in mind, and given the historical pricing data I just uploaded, do you think any of these stands out as clearly a “very high” priced model?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "battery_power": "1439",
+          "blue": "0",
+          "clock_speed": "0.9",
+          "dual_sim": "0",
+          "fc": "12",
+          "four_g": "1",
+          "int_memory": "20",
+          "m_dep": "0.8",
+          "mobile_wt": "147",
+          "n_cores": "1",
+          "pc": "17",
+          "px_height": "626",
+          "px_width": "932",
+          "ram": "1790",
+          "sc_h": "19",
+          "sc_w": "12",
+          "talk_time": "15",
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "1",
+          "price_range": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "battery_power": "600",
+          "blue": "0",
+          "clock_speed": "2.5",
+          "dual_sim": "1",
+          "fc": "8",
+          "four_g": "1",
+          "int_memory": "22",
+          "m_dep": "0.1",
+          "mobile_wt": "145",
+          "n_cores": "1",
+          "pc": "11",
+          "px_height": "207",
+          "px_width": "1162",
+          "ram": "3441",
+          "sc_h": "19",
+          "sc_w": "1",
+          "talk_time": "5",
+          "three_g": "1",
+          "touch_screen": "0",
+          "wifi": "0",
+          "price_range": "2"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "battery_power": "502",
+          "blue": "0",
+          "clock_speed": "0.8",
+          "dual_sim": "0",
+          "fc": "7",
+          "four_g": "0",
+          "int_memory": "52",
+          "m_dep": "1.0",
+          "mobile_wt": "82",
+          "n_cores": "6",
+          "pc": "8",
+          "px_height": "281",
+          "px_width": "1159",
+          "ram": "2666",
+          "sc_h": "5",
+          "sc_w": "4",
+          "talk_time": "20",
+          "three_g": "1",
+          "touch_screen": "1",
+          "wifi": "0",
+          "price_range": "2"
+        }
+      }
+    ],
+    "target_column": "price_range",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/current.csv

@@ -0,0 +1,15 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1132,0,1.0,0,0,0,8,0.1,157,1,4,1091,1293,1950,11,2,6,0,1,1,1
+1306,1,2.1,1,2,1,33,0.4,174,3,9,867,1258,2521,6,5,16,1,0,0,2
+1456,0,1.6,1,5,0,49,0.2,193,3,20,1285,1427,3624,12,11,16,1,1,0,3
+744,0,1.7,1,0,1,33,0.5,105,4,2,1252,1963,2700,9,3,19,1,0,0,2
+1076,1,2.6,0,3,1,38,0.7,119,6,8,129,915,891,18,3,9,1,1,0,0
+1719,1,2.0,0,0,0,12,0.3,122,8,7,240,904,462,6,3,5,0,0,0,0
+1312,1,0.8,1,0,0,24,0.9,156,6,0,115,1791,3833,14,13,17,0,1,0,3
+1820,1,1.4,0,5,0,51,0.9,163,7,10,202,1884,2481,11,9,11,0,0,0,2
+1593,1,1.0,0,0,1,52,0.7,130,8,0,761,1336,1354,15,13,13,1,1,1,1
+1867,0,2.3,0,0,1,9,0.1,191,6,3,712,1442,990,6,1,2,1,0,1,1
+899,1,2.7,0,3,1,53,0.3,192,4,11,641,1638,2870,19,16,10,1,0,1,2
+1439,0,0.9,0,12,1,20,0.8,147,1,17,626,932,1790,19,12,15,1,0,1,1
+600,0,2.5,1,8,1,22,0.1,145,1,11,207,1162,3441,19,1,5,1,0,0,2
+502,0,0.8,0,7,0,52,1.0,82,6,8,281,1159,2666,5,4,20,1,1,0,2

decision_making/daily/classification/Mobile_Price_Classification_B2/info.json

@@ -0,0 +1,130 @@
+{
+    "name": "Mobile Price Classification",
+    "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+    "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+    "is_splited": true,
+    "overall_size": 3000,
+    "train_size": 2000,
+    "test_size": 1000,
+    "c_classes": 0,
+    "n_classes": 22,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "id": "Unique identifier for each mobile phone",
+        "battery_power": "Total energy capacity of the battery in mAh",
+        "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+        "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+        "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+        "fc": "Front Camera megapixels",
+        "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+        "int_memory": "Internal memory in Gigabytes",
+        "m_dep": "Mobile depth in cm",
+        "mobile_wt": "Weight of the mobile phone in grams",
+        "n_cores": "Number of cores in the processor",
+        "pc": "Primary Camera megapixels",
+        "px_height": "Pixel resolution height",
+        "px_width": "Pixel resolution width",
+        "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+        "sc_h": "Screen height of the mobile in cm",
+        "sc_w": "Screen width of the mobile in cm",
+        "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+        "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+        "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+        "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)",
+        "price_range": "Price range of the mobile (0: Low, 1: Medium, 2: High, 3: Very High)"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "price_range",
+    "num_feature_value": {
+        "battery_power": [
+            500.0,
+            1999.0
+        ],
+        "blue": [
+            0.0,
+            1.0
+        ],
+        "clock_speed": [
+            0.5,
+            3.0
+        ],
+        "dual_sim": [
+            0.0,
+            1.0
+        ],
+        "fc": [
+            0.0,
+            19.0
+        ],
+        "four_g": [
+            0.0,
+            1.0
+        ],
+        "id": [
+            1.0,
+            1000.0
+        ],
+        "int_memory": [
+            2.0,
+            64.0
+        ],
+        "m_dep": [
+            0.1,
+            1.0
+        ],
+        "mobile_wt": [
+            80.0,
+            200.0
+        ],
+        "n_cores": [
+            1.0,
+            8.0
+        ],
+        "pc": [
+            0.0,
+            20.0
+        ],
+        "price_range": [
+            0.0,
+            3.0
+        ],
+        "px_height": [
+            0.0,
+            1960.0
+        ],
+        "px_width": [
+            500.0,
+            1998.0
+        ],
+        "ram": [
+            256.0,
+            3998.0
+        ],
+        "sc_h": [
+            5.0,
+            19.0
+        ],
+        "sc_w": [
+            0.0,
+            18.0
+        ],
+        "talk_time": [
+            2.0,
+            20.0
+        ],
+        "three_g": [
+            0.0,
+            1.0
+        ],
+        "touch_screen": [
+            0.0,
+            1.0
+        ],
+        "wifi": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/info_mod.json

@@ -0,0 +1,190 @@
+{
+  "name": "Mobile Price Classification",
+  "source": "https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification/data",
+  "data_intro": "This dataset contain information about many mobiles anda variables about it,containning some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.",
+  "is_splited": true,
+  "overall_size": 3000,
+  "train_size": 2000,
+  "test_size": 1000,
+  "c_classes": 0,
+  "n_classes": 22,
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "battery_power": "Total energy capacity of the battery in mAh",
+    "blue": "Whether the phone has Bluetooth (1: Yes, 0: No)",
+    "clock_speed": "Speed at which microprocessor executes instructions (in GHz)",
+    "dual_sim": "Whether the phone supports dual SIM (1: Yes, 0: No)",
+    "fc": "Front Camera megapixels",
+    "four_g": "Whether the phone has 4G capability (1: Yes, 0: No)",
+    "int_memory": "Internal memory in Gigabytes",
+    "m_dep": "Mobile depth in cm",
+    "mobile_wt": "Weight of the mobile phone in grams",
+    "n_cores": "Number of cores in the processor",
+    "pc": "Primary Camera megapixels",
+    "px_height": "Pixel resolution height",
+    "px_width": "Pixel resolution width",
+    "ram": "Random Access Memory in Megabytes. Bob wants to find out some relation between features of a mobile phone (e.g., RAM, Internal Memory, etc.) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.",
+    "sc_h": "Screen height of the mobile in cm",
+    "sc_w": "Screen width of the mobile in cm",
+    "talk_time": "Maximum time that a single battery charge will last when the phone is used continuously in hours",
+    "three_g": "Whether the phone has 3G capability (1: Yes, 0: No)",
+    "touch_screen": "Whether the phone has a touch screen (1: Yes, 0: No)",
+    "wifi": "Whether the phone has WiFi capability (1: Yes, 0: No)"
+  },
+  "evaluation_metric": null,
+  "task_type": "classification",
+  "target": "price_range",
+  "num_feature_value": {
+    "battery_power": [
+      500.0,
+      1999.0
+    ],
+    "blue": [
+      0.0,
+      1.0
+    ],
+    "clock_speed": [
+      0.5,
+      3.0
+    ],
+    "dual_sim": [
+      0.0,
+      1.0
+    ],
+    "fc": [
+      0.0,
+      19.0
+    ],
+    "four_g": [
+      0.0,
+      1.0
+    ],
+    "int_memory": [
+      2.0,
+      64.0
+    ],
+    "m_dep": [
+      0.1,
+      1.0
+    ],
+    "mobile_wt": [
+      80.0,
+      200.0
+    ],
+    "n_cores": [
+      1.0,
+      8.0
+    ],
+    "pc": [
+      0.0,
+      20.0
+    ],
+    "px_height": [
+      0.0,
+      1960.0
+    ],
+    "px_width": [
+      500.0,
+      1998.0
+    ],
+    "ram": [
+      256.0,
+      3998.0
+    ],
+    "sc_h": [
+      5.0,
+      19.0
+    ],
+    "sc_w": [
+      0.0,
+      18.0
+    ],
+    "talk_time": [
+      2.0,
+      20.0
+    ],
+    "three_g": [
+      0.0,
+      1.0
+    ],
+    "touch_screen": [
+      0.0,
+      1.0
+    ],
+    "wifi": [
+      0.0,
+      1.0
+    ]
+  },
+  "cat_feature_value": {},
+  "columns": [
+    "battery_power",
+    "blue",
+    "clock_speed",
+    "dual_sim",
+    "fc",
+    "four_g",
+    "int_memory",
+    "m_dep",
+    "mobile_wt",
+    "n_cores",
+    "pc",
+    "px_height",
+    "px_width",
+    "ram",
+    "sc_h",
+    "sc_w",
+    "talk_time",
+    "three_g",
+    "touch_screen",
+    "wifi",
+    "price_range"
+  ],
+  "feature_columns": [
+    "battery_power",
+    "blue",
+    "clock_speed",
+    "dual_sim",
+    "fc",
+    "four_g",
+    "int_memory",
+    "m_dep",
+    "mobile_wt",
+    "n_cores",
+    "pc",
+    "px_height",
+    "px_width",
+    "ram",
+    "sc_h",
+    "sc_w",
+    "talk_time",
+    "three_g",
+    "touch_screen",
+    "wifi"
+  ],
+  "feature_types": {
+    "battery_power": "numeric",
+    "blue": "numeric",
+    "clock_speed": "numeric",
+    "dual_sim": "numeric",
+    "fc": "numeric",
+    "four_g": "numeric",
+    "int_memory": "numeric",
+    "m_dep": "numeric",
+    "mobile_wt": "numeric",
+    "n_cores": "numeric",
+    "pc": "numeric",
+    "px_height": "numeric",
+    "px_width": "numeric",
+    "ram": "numeric",
+    "sc_h": "numeric",
+    "sc_w": "numeric",
+    "talk_time": "numeric",
+    "three_g": "numeric",
+    "touch_screen": "numeric",
+    "wifi": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/Mobile_Price_Classification_B2/test.csv

@@ -0,0 +1,15 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1132,0,1.0,0,0,0,8,0.1,157,1,4,1091,1293,1950,11,2,6,0,1,1,1
+1306,1,2.1,1,2,1,33,0.4,174,3,9,867,1258,2521,6,5,16,1,0,0,2
+1456,0,1.6,1,5,0,49,0.2,193,3,20,1285,1427,3624,12,11,16,1,1,0,3
+744,0,1.7,1,0,1,33,0.5,105,4,2,1252,1963,2700,9,3,19,1,0,0,2
+1076,1,2.6,0,3,1,38,0.7,119,6,8,129,915,891,18,3,9,1,1,0,0
+1719,1,2.0,0,0,0,12,0.3,122,8,7,240,904,462,6,3,5,0,0,0,0
+1312,1,0.8,1,0,0,24,0.9,156,6,0,115,1791,3833,14,13,17,0,1,0,3
+1820,1,1.4,0,5,0,51,0.9,163,7,10,202,1884,2481,11,9,11,0,0,0,2
+1593,1,1.0,0,0,1,52,0.7,130,8,0,761,1336,1354,15,13,13,1,1,1,1
+1867,0,2.3,0,0,1,9,0.1,191,6,3,712,1442,990,6,1,2,1,0,1,1
+899,1,2.7,0,3,1,53,0.3,192,4,11,641,1638,2870,19,16,10,1,0,1,2
+1439,0,0.9,0,12,1,20,0.8,147,1,17,626,932,1790,19,12,15,1,0,1,1
+600,0,2.5,1,8,1,22,0.1,145,1,11,207,1162,3441,19,1,5,1,0,0,2
+502,0,0.8,0,7,0,52,1.0,82,6,8,281,1159,2666,5,4,20,1,1,0,2

decision_making/daily/classification/Mobile_Price_Classification_B2/test_001.csv

@@ -0,0 +1,3 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1132,0,1.0,0,0,0,8,0.1,157,1,4,1091,1293,1950,11,2,6,0,1,1,1
+1306,1,2.1,1,2,1,33,0.4,174,3,9,867,1258,2521,6,5,16,1,0,0,2

decision_making/daily/classification/Mobile_Price_Classification_B2/test_002.csv

@@ -0,0 +1,4 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1456,0,1.6,1,5,0,49,0.2,193,3,20,1285,1427,3624,12,11,16,1,1,0,3
+744,0,1.7,1,0,1,33,0.5,105,4,2,1252,1963,2700,9,3,19,1,0,0,2
+1076,1,2.6,0,3,1,38,0.7,119,6,8,129,915,891,18,3,9,1,1,0,0

decision_making/daily/classification/Mobile_Price_Classification_B2/test_003.csv

@@ -0,0 +1,3 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1719,1,2.0,0,0,0,12,0.3,122,8,7,240,904,462,6,3,5,0,0,0,0
+1312,1,0.8,1,0,0,24,0.9,156,6,0,115,1791,3833,14,13,17,0,1,0,3

decision_making/daily/classification/Mobile_Price_Classification_B2/test_004.csv

@@ -0,0 +1,3 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1820,1,1.4,0,5,0,51,0.9,163,7,10,202,1884,2481,11,9,11,0,0,0,2
+1593,1,1.0,0,0,1,52,0.7,130,8,0,761,1336,1354,15,13,13,1,1,1,1

decision_making/daily/classification/Mobile_Price_Classification_B2/test_005.csv

@@ -0,0 +1,3 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1867,0,2.3,0,0,1,9,0.1,191,6,3,712,1442,990,6,1,2,1,0,1,1
+899,1,2.7,0,3,1,53,0.3,192,4,11,641,1638,2870,19,16,10,1,0,1,2

decision_making/daily/classification/Mobile_Price_Classification_B2/test_006.csv

@@ -0,0 +1,4 @@
+battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range
+1439,0,0.9,0,12,1,20,0.8,147,1,17,626,932,1790,19,12,15,1,0,1,1
+600,0,2.5,1,8,1,22,0.1,145,1,11,207,1162,3441,19,1,5,1,0,0,2
+502,0,0.8,0,7,0,52,1.0,82,6,8,281,1159,2666,5,4,20,1,1,0,2

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_001.json

@@ -0,0 +1,61 @@
+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "My friend insists one of these is a superb wine, but staring at the labels makes my head spin. Taking the bottle with ID 1279, its fixed acidity is 9.8, volatile acidity is 0.3, and citric acid measures 0.39. It has 1.7 residual sugar, chlorides at 0.062, and sulfur dioxide levels are quite low at 3.0 free and 9.0 total. The density is 0.9948, acidity pH is 3.14, sulphates are 0.57, and alcohol is 11.5. The other, ID 769, lists fixed acidity as 7.9, volatile acidity jumps to 0.72, while citric acid is barely there at 0.01. Residual sugar is 1.9, chlorides are 0.076, and it has more sulfur dioxide with 7.0 free and 32.0 total. This one's density is a bit higher at 0.99668, pH is 3.39, sulphates are 0.54, and alcohol content is 9.6. With all these details swimming around, picking the truly excellent one feels like a puzzle. Is there a clear winner for the 7-quality wine here? ",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "9.8",
+          "volatile acidity": "0.3",
+          "citric acid": "0.39",
+          "residual sugar": "1.7",
+          "chlorides": "0.062",
+          "free sulfur dioxide": "3.0",
+          "total sulfur dioxide": "9.0",
+          "density": "0.9948",
+          "pH": "3.14",
+          "sulphates": "0.57",
+          "alcohol": "11.5",
+          "quality": "7",
+          "Id": "1279"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "7.9",
+          "volatile acidity": "0.72",
+          "citric acid": "0.01",
+          "residual sugar": "1.9",
+          "chlorides": "0.076",
+          "free sulfur dioxide": "7.0",
+          "total sulfur dioxide": "32.0",
+          "density": "0.99668",
+          "pH": "3.39",
+          "sulphates": "0.54",
+          "alcohol": "9.6",
+          "quality": "5",
+          "Id": "769"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_002.json

@@ -0,0 +1,61 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "Choosing between these is tricky. The first one, labeled 1361, has a fixed acidity of 8.3, volatile acidity of 0.85, citric acid at 0.14, residual sugar 2.5, and free sulfur dioxide at 13.0. Its density is 0.99724, pH is 3.36, sulphates are 0.54, and alcohol is 10.1. Meanwhile, the second, ID 1452, reports fixed acidity 6.6, volatile acidity 0.58, citric acid 0.02, chlorides 0.062, free sulfur dioxide 37.0, total sulfur dioxide 53.0, density 0.99374, and pH 3.35. I'm hesitating because their chemical balances are so distinct. To get a wine that's most likely a 5 in quality, should I grab bottle 1361 or 1452?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "8.3",
+          "volatile acidity": "0.85",
+          "citric acid": "0.14",
+          "residual sugar": "2.5",
+          "chlorides": null,
+          "free sulfur dioxide": "13.0",
+          "total sulfur dioxide": null,
+          "density": "0.99724",
+          "pH": "3.36",
+          "sulphates": "0.54",
+          "alcohol": "10.1",
+          "quality": "5",
+          "Id": "1361"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "6.6",
+          "volatile acidity": "0.58",
+          "citric acid": "0.02",
+          "residual sugar": null,
+          "chlorides": "0.062",
+          "free sulfur dioxide": "37.0",
+          "total sulfur dioxide": "53.0",
+          "density": "0.99374",
+          "pH": "3.35",
+          "sulphates": null,
+          "alcohol": null,
+          "quality": "7",
+          "Id": "1452"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_003.json

@@ -0,0 +1,61 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "Both these wines have their merits, but finding the standout is tricky. For the first, ID 1451, the fixed acidity is 7.8, volatile acidity 0.32, citric acid 0.44, and residual sugar sits at 2.7. The chlorides measure 0.104, free sulfur dioxide is 8.0, total sulfur dioxide 17.0, and density is 0.99732. Its pH is 3.33, sulphates are 0.78, and alcohol content is 11.0. The second, ID 517, reports fixed acidity 10.4, volatile acidity 0.61, citric acid 0.49, and residual sugar 2.1. Chlorides are 0.2, free sulfur dioxide 5.0, total sulfur dioxide 16.0, density 0.9994, pH 3.16, sulphates 0.63, and alcohol 8.4. After laying all that out, I'm still on the fence—am I better off selecting the first or the second if 3 quality is the goal?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "7.8",
+          "volatile acidity": "0.32",
+          "citric acid": "0.44",
+          "residual sugar": "2.7",
+          "chlorides": "0.104",
+          "free sulfur dioxide": "8.0",
+          "total sulfur dioxide": "17.0",
+          "density": "0.99732",
+          "pH": "3.33",
+          "sulphates": "0.78",
+          "alcohol": "11.0",
+          "quality": "7",
+          "Id": "1451"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "10.4",
+          "volatile acidity": "0.61",
+          "citric acid": "0.49",
+          "residual sugar": "2.1",
+          "chlorides": "0.2",
+          "free sulfur dioxide": "5.0",
+          "total sulfur dioxide": "16.0",
+          "density": "0.9994",
+          "pH": "3.16",
+          "sulphates": "0.63",
+          "alcohol": "8.4",
+          "quality": "3",
+          "Id": "517"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_004.json

@@ -0,0 +1,79 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "With a big family dinner coming up, choosing the right wine feels crucial—I want it to be a hit! From my notes, the first wine has a fixed acidity of 9.4, citric acid at 0.56, chlorides 0.08, free sulfur dioxide 6.0, total sulfur dioxide 17.0, density 0.9964, pH 3.15, sulphates 0.92, alcohol 11.7, and it's labeled with Id 481. The second shows fixed acidity 8.9, volatile acidity 0.4, residual sugar 5.6, chlorides 0.087, free sulfur dioxide 10.0, total sulfur dioxide 47.0, density 0.9991, sulphates 0.77, alcohol 10.5, and Id 283. The third records fixed acidity 8.3, volatile acidity 0.845, citric acid 0.01, residual sugar 2.2, chlorides 0.07, free sulfur dioxide 5.0, total sulfur dioxide 14.0, density 0.9967, pH 3.32, and Id 647. I'm going off my old wine quality archives here—can you look at those past trends and tell me which bottle is most likely to be quality level 4?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "9.4",
+          "volatile acidity": null,
+          "citric acid": "0.56",
+          "residual sugar": null,
+          "chlorides": "0.08",
+          "free sulfur dioxide": "6.0",
+          "total sulfur dioxide": "17.0",
+          "density": "0.9964",
+          "pH": "3.15",
+          "sulphates": "0.92",
+          "alcohol": "11.7",
+          "quality": "8",
+          "Id": "481"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "8.9",
+          "volatile acidity": "0.4",
+          "citric acid": null,
+          "residual sugar": "5.6",
+          "chlorides": "0.087",
+          "free sulfur dioxide": "10.0",
+          "total sulfur dioxide": "47.0",
+          "density": "0.9991",
+          "pH": null,
+          "sulphates": "0.77",
+          "alcohol": "10.5",
+          "quality": "7",
+          "Id": "283"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "fixed acidity": "8.3",
+          "volatile acidity": "0.845",
+          "citric acid": "0.01",
+          "residual sugar": "2.2",
+          "chlorides": "0.07",
+          "free sulfur dioxide": "5.0",
+          "total sulfur dioxide": "14.0",
+          "density": "0.9967",
+          "pH": "3.32",
+          "sulphates": null,
+          "alcohol": null,
+          "quality": "4",
+          "Id": "647"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_005.json

@@ -0,0 +1,79 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "All this data is making my head spin a bit before the dinner party. For the first candidate, the numbers are fixed acidity 7.8, volatile acidity 0.6, citric acid 0.26, residual sugar 2.0, chlorides 0.08, free sulfur dioxide 31.0, total sulfur dioxide 131.0, density 0.99622, pH 3.21, sulphates 0.52, alcohol 9.9, and it’s recorded under Id 1560. The second has fixed acidity 8.0, volatile acidity 0.62, citric acid 0.33, residual sugar 2.7, chlorides 0.088, free sulfur dioxide 16.0, total sulfur dioxide 37.0, density 0.9972, pH 3.31, sulphates 0.58, alcohol 10.7, Id 1073. The third lists fixed acidity 6.7, volatile acidity 0.64, citric acid 0.23, residual sugar 2.1, chlorides 0.08, free sulfur dioxide 11.0, total sulfur dioxide 119.0, density 0.99538, pH 3.36, sulphates 0.7, alcohol 10.9, Id 1184. I’ve shared my historical wine quality archive with you—could you pick out which of these three seems most likely to be rated a 6 in quality?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "7.8",
+          "volatile acidity": "0.6",
+          "citric acid": "0.26",
+          "residual sugar": "2.0",
+          "chlorides": "0.08",
+          "free sulfur dioxide": "31.0",
+          "total sulfur dioxide": "131.0",
+          "density": "0.99622",
+          "pH": "3.21",
+          "sulphates": "0.52",
+          "alcohol": "9.9",
+          "quality": "5",
+          "Id": "1560"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "8.0",
+          "volatile acidity": "0.62",
+          "citric acid": "0.33",
+          "residual sugar": "2.7",
+          "chlorides": "0.088",
+          "free sulfur dioxide": "16.0",
+          "total sulfur dioxide": "37.0",
+          "density": "0.9972",
+          "pH": "3.31",
+          "sulphates": "0.58",
+          "alcohol": "10.7",
+          "quality": "6",
+          "Id": "1073"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "fixed acidity": "6.7",
+          "volatile acidity": "0.64",
+          "citric acid": "0.23",
+          "residual sugar": "2.1",
+          "chlorides": "0.08",
+          "free sulfur dioxide": "11.0",
+          "total sulfur dioxide": "119.0",
+          "density": "0.99538",
+          "pH": "3.36",
+          "sulphates": "0.7",
+          "alcohol": "10.9",
+          "quality": "5",
+          "Id": "1184"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/Wine_Quality_Dataset_B2_006.json

@@ -0,0 +1,79 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Wine_Quality_Dataset",
+  "table_path": "kaggle/Wine_Quality_Dataset",
+  "query": "So here’s the situation: three wine samples with all their chemical readings, and I need to figure out the best. Sample 85 has fixed acidity recorded as 6.9, volatile acidity 0.55, citric acid 0.15, residual sugar 2.2, chlorides 0.076, free sulfur dioxide 19.0, total sulfur dioxide 40.0, density 0.9961, pH 3.41, sulphates 0.59, and alcohol 10.1. Sample 806 shows fixed acidity 8.4, volatile acidity 0.25, citric acid 0.39, residual sugar 2.0, chlorides 0.0409999999999999, free sulfur dioxide 4.0, total sulfur dioxide 10.0, density 0.99386, pH 3.27, sulphates 0.71, alcohol 12.5. Lastly, sample 390 gives fixed acidity 5.6, volatile acidity 0.85, citric acid 0.05, residual sugar 1.4, chlorides 0.045, free sulfur dioxide 12.0, total sulfur dioxide 88.0, density 0.9924, pH 3.56, sulphates 0.82, alcohol 12.9. I’ve shared our historical wine archives—based on that past data, does one of these stand out as likely to be quality 8?",
+  "meta_info": {
+    "domain": "daily"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "fixed acidity": "6.9",
+          "volatile acidity": "0.55",
+          "citric acid": "0.15",
+          "residual sugar": "2.2",
+          "chlorides": "0.076",
+          "free sulfur dioxide": "19.0",
+          "total sulfur dioxide": "40.0",
+          "density": "0.9961",
+          "pH": "3.41",
+          "sulphates": "0.59",
+          "alcohol": "10.1",
+          "quality": "5",
+          "Id": "85"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "fixed acidity": "8.4",
+          "volatile acidity": "0.25",
+          "citric acid": "0.39",
+          "residual sugar": "2.0",
+          "chlorides": "0.0409999999999999",
+          "free sulfur dioxide": "4.0",
+          "total sulfur dioxide": "10.0",
+          "density": "0.99386",
+          "pH": "3.27",
+          "sulphates": "0.71",
+          "alcohol": "12.5",
+          "quality": "7",
+          "Id": "806"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "fixed acidity": "5.6",
+          "volatile acidity": "0.85",
+          "citric acid": "0.05",
+          "residual sugar": "1.4",
+          "chlorides": "0.045",
+          "free sulfur dioxide": "12.0",
+          "total sulfur dioxide": "88.0",
+          "density": "0.9924",
+          "pH": "3.56",
+          "sulphates": "0.82",
+          "alcohol": "12.9",
+          "quality": "8",
+          "Id": "390"
+        }
+      }
+    ],
+    "target_column": "quality",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/current.csv

@@ -0,0 +1,16 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,Id
+9.8,0.3,0.39,1.7,0.062,3.0,9.0,0.9948,3.14,0.57,11.5,7,1279
+7.9,0.72,0.01,1.9,0.076,7.0,32.0,0.99668,3.39,0.54,9.6,5,769
+8.3,0.85,0.14,2.5,0.093,13.0,54.0,0.99724,3.36,0.54,10.1,5,1361
+6.6,0.58,0.02,2.0,0.062,37.0,53.0,0.99374,3.35,0.76,11.6,7,1452
+7.8,0.32,0.44,2.7,0.104,8.0,17.0,0.99732,3.33,0.78,11.0,7,1451
+10.4,0.61,0.49,2.1,0.2,5.0,16.0,0.9994,3.16,0.63,8.4,3,517
+9.4,0.3,0.56,2.8,0.08,6.0,17.0,0.9964,3.15,0.92,11.7,8,481
+8.9,0.4,0.32,5.6,0.087,10.0,47.0,0.9991,3.38,0.77,10.5,7,283
+8.3,0.845,0.01,2.2,0.07,5.0,14.0,0.9967,3.32,0.58,11.0,4,647
+7.8,0.6,0.26,2.0,0.08,31.0,131.0,0.99622,3.21,0.52,9.9,5,1560
+8.0,0.62,0.33,2.7,0.088,16.0,37.0,0.9972,3.31,0.58,10.7,6,1073
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decision_making/daily/classification/Wine_Quality_Dataset_B2/info.json

@@ -0,0 +1,86 @@
+{
+    "name": "Wine Quality Dataset",
+    "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data",
+    "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.",
+    "is_splited": false,
+    "overall_size": 1143,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 0,
+    "n_classes": 13,
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "fixed acidity": "fixed acidity",
+        "volatile acidity": "volatile acidity",
+        "citric acid": "citric acid",
+        "residual sugar": "residual sugar",
+        "chlorides": "chlorides",
+        "free sulfur dioxide": "free sulfur dioxide",
+        "total sulfur dioxide": "total sulfur dioxide",
+        "density": "density",
+        "pH": "pH",
+        "sulphates": "sulphates",
+        "alcohol": "alcohol",
+        "quality": "the quality of wine(classes are ordered and imbalanced)",
+        "Id": "identifier for each sample"
+    },
+    "evaluation_metric": null,
+    "task_type": "classification",
+    "target": "quality",
+    "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality",
+    "num_feature_value": {
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+            1597.0
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+        "alcohol": [
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+        "free sulfur dioxide": [
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+            68.0
+        ],
+        "pH": [
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+            4.01
+        ],
+        "quality": [
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+        ],
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+        "total sulfur dioxide": [
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+            289.0
+        ],
+        "volatile acidity": [
+            0.12,
+            1.58
+        ]
+    },
+    "cat_feature_value": {}
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/info_mod.json

@@ -0,0 +1,127 @@
+{
+  "name": "Wine Quality Dataset",
+  "source": "https://www.kaggle.com/datasets/yasserh/wine-quality-dataset/data",
+  "data_intro": "This datasets is related to red variants of the Portuguese \"Vinho Verde\" wine.The dataset describes the amount of various chemicals present in wine and their effect on it's quality. The datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.",
+  "is_splited": false,
+  "overall_size": 1143,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 0,
+  "n_classes": 13,
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "fixed acidity": "fixed acidity",
+    "volatile acidity": "volatile acidity",
+    "citric acid": "citric acid",
+    "residual sugar": "residual sugar",
+    "chlorides": "chlorides",
+    "free sulfur dioxide": "free sulfur dioxide",
+    "total sulfur dioxide": "total sulfur dioxide",
+    "density": "density",
+    "pH": "pH",
+    "sulphates": "sulphates",
+    "alcohol": "alcohol",
+    "Id": "identifier for each sample"
+  },
+  "evaluation_metric": null,
+  "task_type": "classification",
+  "target": "quality",
+  "example": "https://www.kaggle.com/code/qusaybtoush1990/wine-quality",
+  "num_feature_value": {
+    "fixed acidity": [
+      4.6,
+      15.9
+    ],
+    "volatile acidity": [
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+      1.58
+    ],
+    "citric acid": [
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+      1.0
+    ],
+    "residual sugar": [
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+    "chlorides": [
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+      0.611
+    ],
+    "free sulfur dioxide": [
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+      68.0
+    ],
+    "total sulfur dioxide": [
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+      289.0
+    ],
+    "density": [
+      0.99007,
+      1.00369
+    ],
+    "pH": [
+      2.74,
+      4.01
+    ],
+    "sulphates": [
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+      2.0
+    ],
+    "alcohol": [
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+      14.9
+    ],
+    "Id": [
+      0.0,
+      1597.0
+    ]
+  },
+  "cat_feature_value": {},
+  "columns": [
+    "fixed acidity",
+    "volatile acidity",
+    "citric acid",
+    "residual sugar",
+    "chlorides",
+    "free sulfur dioxide",
+    "total sulfur dioxide",
+    "density",
+    "pH",
+    "sulphates",
+    "alcohol",
+    "quality",
+    "Id"
+  ],
+  "feature_columns": [
+    "fixed acidity",
+    "volatile acidity",
+    "citric acid",
+    "residual sugar",
+    "chlorides",
+    "free sulfur dioxide",
+    "total sulfur dioxide",
+    "density",
+    "pH",
+    "sulphates",
+    "alcohol",
+    "Id"
+  ],
+  "feature_types": {
+    "fixed acidity": "numeric",
+    "volatile acidity": "numeric",
+    "citric acid": "numeric",
+    "residual sugar": "numeric",
+    "chlorides": "numeric",
+    "free sulfur dioxide": "numeric",
+    "total sulfur dioxide": "numeric",
+    "density": "numeric",
+    "pH": "numeric",
+    "sulphates": "numeric",
+    "alcohol": "numeric",
+    "Id": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/daily/classification/Wine_Quality_Dataset_B2/test.csv

@@ -0,0 +1,16 @@
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