id int64 0 7.95k | code_block stringlengths 1 52.9k | too_long stringclasses 2
values | marks int64 1 5 | graph_vertex_id float64 1 88 | graph_vertex_class stringclasses 12
values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3,412 | pred = knn.predict(test_data.drop("Id",axis=1)) | Yes | 4 | 7 | Model_Train | train_model | 10,550,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
3,413 | submission = pd.DataFrame(data=pred,columns=["Cover_Type"])
submission["Id"] = test_data["Id"]
submission.set_index("Id",inplace=True) | No | 5 | 55 | Data_Export | prepare_output | 10,550,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,414 | submission.to_csv("Submission.csv") | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,415 | import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,auc
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split | No | 5 | 22 | Environment | import_modules | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 5 | A |
3,416 | df_train = pd.read_csv('/kaggle/input/forest-cover-type-prediction/train.csv')
df_test = pd.read_csv('/kaggle/input/forest-cover-type-prediction/test.csv')
| No | 5 | 45 | Data_Extraction | load_from_csv | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 5 | A |
3,417 | df_train.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,418 | test_id =df_test['Id']
train_id = df_train ['Id'] | No | 5 | 77 | Other | define_variables | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 5 | A |
3,419 | df_train.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,420 | df_test.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,421 | df_test.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,422 | df_test.dtypes | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,423 | # From both train and test data
df_train.drop(['Id'], axis = 1,inplace = True)
df_test.drop(['Id'], axis = 1,inplace = True) | No | 5 | 10 | Data_Transform | drop_column | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "InPlace Checker"] | 5 | B |
3,424 | sns.heatmap(df_train.isnull()) | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,425 | sns.heatmap(df_test.isnull()) | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,426 | corrmat = df_train.corr()
sns.heatmap(corrmat,vmax = 0.8,square = True) | No | 5 | 80 | Visualization | heatmap | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker"] | 5 | B |
3,427 | data_corr.head() | No | 5 | 41 | Debug | show_table | 10,760,317 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 5 | A |
3,428 | plt.figure(figsize=(15,10))
pd.crosstab(train.Wilderness_Area, train.Cover_Type).plot.barh(figsize=(15,15),stacked = True) | No | 5 | 33 | Visualization | distribution | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 8 | B |
3,429 | plt.figure(figsize=(15,10))
pd.crosstab(train.soil_type, train.Cover_Type).plot.barh(figsize=(15,15),stacked = True) | No | 5 | 33 | Visualization | distribution | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 8 | B |
3,430 | plt.subplots(figsize=(10,10))
corr = df_viz.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sns.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);
| No | 5 | 80 | Visualization | heatmap | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
3,431 | def add_feature(data):
data['Ele_minus_VDtHyd'] = data.Elevation-data.Vertical_Distance_To_Hydrology
data['Ele_plus_VDtHyd'] = data.Elevation+data.Vertical_Distance_To_Hydrology
data['Distanse_to_Hydrolody'] = (data['Horizontal_Distance_To_Hydrology']**2+data['Vertical_Distance_To_Hydrology']**2)**0.5
... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,432 | train = add_feature(train)
test = add_feature(test) | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,433 | X_train = train.drop(['Id','Cover_Type','soil_type','Wilderness_Area'], axis = 1)
y_train = train.Cover_Type
X_test = test.drop(['Id'], axis = 1) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 8 | B |
3,434 | %%time
lr_pipe = Pipeline(
steps = [
('scaler', MinMaxScaler()),
('classifier', LogisticRegression(solver='lbfgs', n_jobs=-1))
]
)
lr_param_grid = {
'classifier__C': [1, 10, 100,1000],
}
np.random.seed(1)
grid_search = GridSearchCV(lr_pipe, lr_param_grid, cv=5, refit='True')
grid_search... | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | 8,895,321 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 8 | null |
3,435 | %%time
rf_pipe = Pipeline(
steps = [
('classifier', RandomForestClassifier(n_estimators=500))
]
)
param_grid = {
'classifier__min_samples_leaf': [2, 3, 4, 8],
'classifier__max_depth': [30, 32, 34],
}
np.random.seed(1)
rf_grid_search = GridSearchCV(rf_pipe, param_grid, cv=5, refit='True', n_j... | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | 8,895,321 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 8 | null |
3,436 | rf_model = rf_grid_search.best_estimator_
cv_score = cross_val_score(rf_model, X_train, y_train, cv = 5)
print(cv_score)
print("Accuracy: %0.2f (+/- %0.2f)" % (cv_score.mean(), cv_score.std() * 2)) | Yes | 5 | 1 | Hyperparam_Tuning | find_best_score | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,437 | rf = rf_grid_search.best_estimator_.steps[0][1] | No | 3 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,438 | feat_imp = rf.feature_importances_
feat_imp_df = pd.DataFrame({
'feature':X_train.columns,
'feat_imp':feat_imp
})
feat_imp_df.sort_values(by='feat_imp', ascending=False).head(10) | Yes | 5 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,439 | sorted_feat_imp_df = feat_imp_df.sort_values(by='feat_imp', ascending=True)
plt.figure(figsize=[6,6])
plt.barh(sorted_feat_imp_df.feature[-20:], sorted_feat_imp_df.feat_imp[-20:])
plt.show() | No | 5 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,440 | %%time
xgd_pipe = Pipeline(
steps = [
('classifier', XGBClassifier(n_estimators=50, subsample=0.5))
]
)
param_grid = {
'classifier__learning_rate' : [0.45],
'classifier__min_samples_split' : [8, 16, 32],
'classifier__min_samples_leaf' : [2],
'classifier__max_depth': [15]
}
np.ra... | Yes | 4 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,441 | xgd_model = xgd_grid_search.best_estimator_
cv_score = cross_val_score(xgd_model, X_train, y_train, cv = 5)
print(cv_score)
print("Accuracy: %0.2f (+/- %0.2f)" % (cv_score.mean(), cv_score.std() * 2)) | Yes | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,442 | final_model = xgd_grid_search.best_estimator_.steps[0][1] | No | 5 | 3 | Hyperparam_Tuning | find_best_model_class | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexing", "fix": "Use single indexing or .loc[], .iloc[] methods.", "benefits": "Enhances code readability and prevents performance issues.", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Chain Indexing"] | 8 | A |
3,443 | final_model.fit(X_train, y_train)
y_pred = final_model.predict(X_test) | Yes | 4 | 7 | Model_Train | train_model | 8,895,321 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 8 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
3,444 | print(len(test.Id)) | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,445 | print(len(y_pred)) | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,446 | from collections import Counter
Counter(y_pred) | Yes | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,447 | submission_sample.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,448 | submission = pd.DataFrame({'Id': test.Id,
'Cover_Type': y_pred})
submission.head() | Yes | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,449 | test_data['Slope'].plot(kind='hist') | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,450 | train_data['Elevation'].plot(kind='hist') | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,451 | X=train_data.drop(labels=['Id','Cover_Type'],axis=1) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,452 | y=train_data['Cover_Type'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,453 | X_train,X_val,y_train,y_val=train_test_split(X,y,random_state=40) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,454 | rfc=RandomForestClassifier(n_estimators=70)
rfc.fit(X_train,y_train)
| Yes | 5 | 7 | Model_Train | train_model | 12,086,305 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
3,455 | Submission=pd.DataFrame(data=predict,columns=['Cover_type'])
Submission.head() | Yes | 5 | 55 | Data_Export | prepare_output | 12,086,305 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,456 | Submission['Id']=test_data['Id']
Submission.set_index('Id',inplace=True)
Submission.head() | Yes | 5 | 55 | Data_Export | prepare_output | 12,086,305 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,458 | dataset_train = pd.read_csv('/kaggle/input/forest-cover-type-prediction/train.csv')
dataset_test = pd.read_csv('/kaggle/input/forest-cover-type-prediction/test.csv')
dataset_train_copy = dataset_train.copy()
dataset_test_copy = dataset_test.copy() | No | 4 | 45 | Data_Extraction | load_from_csv | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 4 | B |
3,459 | dataset_train.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,460 | dataset_test.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,461 | dataset_train_copy.drop('Id', axis=1, inplace=True)
dataset_test_copy.drop('Id', axis=1, inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 5"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["InPlace Checker"] | 4 | B |
3,462 | X = dataset_train_copy.iloc[:, :-1].values
y = dataset_train_copy.iloc[:, -1].values
X_submission = dataset_test_copy.iloc[:, :].values | No | 3 | 21 | Data_Transform | prepare_x_and_y | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,463 | from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X)
X_submission = sc.transform(X_submission)
y_train = y | Yes | 5 | 18 | Data_Transform | normalization | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 9 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | D |
3,464 | from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 30, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train) | Yes | 5 | 7 | Model_Train | train_model | 13,246,053 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
3,465 | y_submission = classifier.predict(X_submission)
dataset_submission = pd.DataFrame({'Id':dataset_test.iloc[:,0], 'Cover_Type': y_submission})
dataset_submission.set_index('Id', inplace=True) | Yes | 5 | 55 | Data_Export | prepare_output | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
3,466 | dataset_submission | No | 5 | 41 | Debug | show_table | 13,246,053 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,467 | dataset_submission.to_csv('Submission.csv') | No | 5 | 25 | Data_Export | save_to_csv | 13,246,053 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,468 | train_df=pd.read_csv('/kaggle/input/forest-cover-type-prediction/train.csv')
train_df.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path"] | 4 | B |
3,469 | test_df=pd.read_csv('/kaggle/input/forest-cover-type-prediction/test.csv')
test_df.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/forest-cover-type-prediction/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 4 | A |
3,470 | train_df.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,471 | test_df.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,472 | test_id=test_df['Id'] | No | 3 | 14 | Data_Transform | filter | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,473 | train_df.drop(['Id'],axis=1,inplace=True)
test_df.drop(['Id'],axis=1,inplace=True) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,474 | X=train_df.drop(['Cover_Type'],axis=1)
y=train_df['Cover_Type'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,475 | X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=40) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,476 | from sklearn.neighbors import KNeighborsClassifier
KNN=KNeighborsClassifier(n_neighbors=6)
KNN.fit(X_train,y_train) | Yes | 5 | 7 | Model_Train | train_model | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
3,477 | KNN.score(X_test,y_test) | No | 5 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,478 | pred=KNN.predict(test_df) | No | 5 | 48 | Model_Evaluation | predict_on_test | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
3,479 | result=pd.DataFrame(data=pred,columns=['Cover_Type']) | No | 5 | 12 | Data_Transform | create_dataframe | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,480 | result['Id']=test_id
result.set_index('Id',inplace=True)
result.head() | Yes | 3 | 55 | Data_Export | prepare_output | 13,676,351 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
3,481 | result.to_csv('Submission.csv') | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,482 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | Yes | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 7 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
3,483 | test_data = pd.read_csv('/kaggle/input/forest-cover-type-prediction/test.csv')
test_data.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/forest-cover-type-prediction/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 4 | A |
3,484 | X = train_data.drop(labels = ['Id','Cover_Type'],axis = 1)
Y = train_data['Cover_Type'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
3,485 | X_train,X_val,Y_train,Y_val = train_test_split(X,Y,random_state = 40) | No | 5 | 13 | Data_Transform | split | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 4 | B |
3,486 | print(X_train.shape,Y_train.shape)
print(X_val.shape,Y_val.shape) | No | 5 | 58 | Debug | show_shape | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,487 | rfc=RandomForestClassifier(n_estimators=70)
rfc.fit(X_train,Y_train) | Yes | 5 | 7 | Model_Train | train_model | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
3,488 | rfc.score(X_val,Y_val) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 4 | A |
3,489 | Submission['Id']=test_data['Id']
Submission.set_index('Id',inplace=True) | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,490 | Submission.to_csv('Submission_first_time.csv') | No | 5 | 25 | Data_Export | save_to_csv | 14,012,397 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
3,491 | train_data.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,492 | KNN = KNeighborsClassifier(n_neighbors = 11, n_jobs = -1)
KNN.fit(X_train,y_train) | Yes | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,493 | KNN.score(X_val,y_val) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 14,114,937 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 4 | A |
3,494 | predict=KNN.predict(test_data.drop(labels=['Id'],axis=1)) | No | 5 | 48 | Model_Evaluation | predict_on_test | 14,114,937 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
3,495 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
3,496 | Submission['Id']=test_data['Id']
Submission.set_index('Id',inplace=True)
| No | 5 | 55 | Data_Export | prepare_output | 14,668,756 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
3,497 | import numpy as np
import pandas as pd
from sklearn import cross_validation, grid_search, linear_model, metrics, pipeline, preprocessing | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
3,498 | def rmsle(y, y_):
log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))
log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))
calc = (log1 - log2) ** 2
return np.sqrt(np.mean(calc)) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,499 | data = pd.read_csv("../input/train.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,500 | data.head(3) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,501 | data.isnull().values.any() | No | 4 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,502 | data.datetime = data.datetime.apply(pd.to_datetime)
data['month'] = data.datetime.apply(lambda x : x.month)
data['hour'] = data.datetime.apply(lambda x : x.hour)
data.head() | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,503 | train_data = data.iloc[:-1000, :]
test_data = data.iloc[-1000:, :]
print(data.shape, train_data.shape, test_data.shape)
| Yes | 4 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,504 | train_labels = train_data['count'].values
train_data = train_data.drop(['datetime', 'count', 'casual', 'registered'], axis = 1)
test_labels = test_data['count'].values
test_data = test_data.drop(['datetime', 'count', 'casual', 'registered'], axis = 1) | No | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,505 | binary_data_columns = ['holiday', 'workingday']
binary_data_indices = np.array([(column in binary_data_columns) for column in train_data.columns], dtype = bool)
categorical_data_columns = ['season', 'weather', 'month']
categorical_data_indices = np.array([(column in categorical_data_columns) for column in train_data.... | No | 4 | 37 | EDA | count_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,506 | transformer_list = [
#binary
('binary_variables_processing', preprocessing.FunctionTransformer(lambda data: data[:, binary_data_indices])),
#numeric
('numeric_variables_processing', pipeline.Pipeline(steps = [
('selecting', pr... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,507 | regressor = linear_model.Lasso(max_iter = 2000) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,508 | estimator = pipeline.Pipeline(steps = [
('feature_processing', pipeline.FeatureUnion(transformer_list=transformer_list)),
('model_fitting', regressor)
]
)
estimator.fit(train_data, train_labels)
predicted = estimator.predict(test_data)
print("RMSLE: ", rmsle(test_labels, predicted))
print("MAE: ", ... | Yes | 4 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,509 | parameters_grid = {
'model_fitting__alpha' : [0.1, 1, 2, 3, 4, 10, 30]
} | No | 5 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,510 | rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)
grid_cv = grid_search.GridSearchCV(estimator, parameters_grid, scoring = rmsle_scorer, cv = 4)
grid_cv.fit(train_data, train_labels)
predicted = grid_cv.best_estimator_.predict(test_data)
print("RMSLE: ", rmsle(test_labels, predicted))
#print("MAE: ",... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,511 | estimator.get_params().keys() | No | 5 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,512 | from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(random_state = 0, max_depth = 20, n_estimators = 150)
estimator = pipeline.Pipeline(steps = [
('feature_processing', pipeline.FeatureUnion(transformer_list = transformer_list)),
('model_fitting', regressor)
]
)
esti... | Yes | 4 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.