id stringlengths 7 11 | source stringclasses 1
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values | title stringlengths 15 214 | text stringlengths 70 30.5k | url stringlengths 57 160 | tags stringlengths 3 113 | created_at int64 1.22B 1.79B |
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se_47910446 | stackoverflow | MACHINE_LEARNING | Sklearn Gaussian Mixture lock parameters? | Sklearn Gaussian Mixture lock parameters?
I'm trying to fit some gaussians, of which I already have a pretty good idea about the initial parameters (in this case, I'm generating the distributions, so I should always be able to fit these). However, I can't seem to figure out how to force the mean to be e.g. 0 for both ... | https://stackoverflow.com/questions/47910446/sklearn-gaussian-mixture-lock-parameters | python, scikit-learn | 1,513,787,140 |
se_63935621 | stackoverflow | MACHINE_LEARNING | Function for estimation of Gaussian Mixture Model with fixed means in python? | Function for estimation of Gaussian Mixture Model with fixed means in python?
Is there a function for estimation of GMM model with fixed means? So far I have found only sklearn.mixture.GaussianMixture which however takes only initial means, but AFAIK can not fix the means. Is there any alternative? | https://stackoverflow.com/questions/63935621/function-for-estimation-of-gaussian-mixture-model-with-fixed-means-in-python | python, scikit-learn, gmm | 1,600,336,667 |
se_42139319 | stackoverflow | MACHINE_LEARNING | Lock steps (prevent refit) in scikit-learn pipeline | Lock steps (prevent refit) in scikit-learn pipeline
Is there a convenient mechanism for locking steps in a scikit-learn pipeline to prevent them from refitting on pipeline.fit()? For example:
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.... | https://stackoverflow.com/questions/42139319/lock-steps-prevent-refit-in-scikit-learn-pipeline | scikit-learn | 1,486,650,577 |
se_64969066 | stackoverflow | MACHINE_LEARNING | how to silence sklearn warning on _logistic regression | how to silence sklearn warning on _logistic regression
Regression didn't work because data don't correlate. I understand I could increase the number of iterations.
However, in this case I don't want to see the following warning:
lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:939: ConvergenceWarning:
lbf... | https://stackoverflow.com/questions/64969066/how-to-silence-sklearn-warning-on-logistic-regression | scikit-learn, regression, warnings, logistic-regression | 1,606,136,974 |
se_79541286 | stackoverflow | MACHINE_LEARNING | Different Feature Selection Results Between Local (Ubuntu VM) and Databricks Using sklearn's SequentialFeatureSelector | Different Feature Selection Results Between Local (Ubuntu VM) and Databricks Using sklearn's SequentialFeatureSelector
I am migrating from running my machine learning pipeline in VS Code with Ubuntu on a VM into Databricks. When I test the same dataset using the same code, I get different selected features from Se... | https://stackoverflow.com/questions/79541286/different-feature-selection-results-between-local-ubuntu-vm-and-databricks-usi | python, machine-learning, scikit-learn, databricks, feature-selection | 1,743,160,974 |
se_76626629 | stackoverflow | MACHINE_LEARNING | SKlearn classifier's predict_proba doesn't sum to 1 | SKlearn classifier's predict_proba doesn't sum to 1
I have a classifier (in this case, it is the sklearn.MLPClassifier), with which I'm trying to perform classification into one of 18 classes.
The class is thus multi-class, not multi-label. I'm trying to predict only a single class.
I have my training data: X... | https://stackoverflow.com/questions/76626629/sklearn-classifiers-predict-proba-doesnt-sum-to-1 | python, machine-learning, scikit-learn | 1,688,629,888 |
se_66814510 | stackoverflow | MACHINE_LEARNING | DecisiontreeClassifier, why is the sum of values wrong? | DecisiontreeClassifier, why is the sum of values wrong?
I visualized my decisiontreeclassifier and I noticed, that the sum of samples are wrong or formulated differently the 'value' value does not fit with the value of the samples(Screenshot)? Do I misinterpret my Decisiontree? I thought if got 100 samples in my node ... | https://stackoverflow.com/questions/66814510/decisiontreeclassifier-why-is-the-sum-of-values-wrong | python, matplotlib, scikit-learn, decision-tree | 1,616,752,156 |
se_52837387 | stackoverflow | MACHINE_LEARNING | How to fix "E: Package 'libatlas-dev' has no installation candidate" While installing Scipy for Scikit learn? | How to fix "E: Package 'libatlas-dev' has no installation candidate" While installing Scipy for Scikit learn?
how do i fix this problem i gave
sudo apt-get install python-setuptools python-scipy libatlas-dev libatlas3-base
to install scipy for installing scikit learn and got an error like this
R... | https://stackoverflow.com/questions/52837387/how-to-fix-e-package-libatlas-dev-has-no-installation-candidate-while-insta | python, scikit-learn, scipy, ubuntu-18.04 | 1,539,698,728 |
se_59065058 | stackoverflow | MACHINE_LEARNING | Yocto: create recipe for SciKit-learn | Yocto: create recipe for SciKit-learn
I'm attempting to create a recipe for SciKit-Learn from pypi. But it seems that python3 setup.py build is unable to find NumPy, even though i know i have it on the build host (both with pip3 install numpy and bitbake python3-numpy-native).
Right now i have the following in my rec... | https://stackoverflow.com/questions/59065058/yocto-create-recipe-for-scikit-learn | python-3.x, linux, numpy, scikit-learn, yocto | 1,574,839,515 |
se_78672059 | stackoverflow | MACHINE_LEARNING | Problems with selecting OptunaSearchCV pipeline parameters | Problems with selecting OptunaSearchCV pipeline parameters
I have a code with which I iterate over the hyperparameters of both the model itself and the entire pipeline
preprocessor = ColumnTransformer(
[
('OneHotEncoder', OneHotEncoder(drop='if_binary', sparse_output=False), binary_cols),
... | https://stackoverflow.com/questions/78672059/problems-with-selecting-optunasearchcv-pipeline-parameters | machine-learning, scikit-learn, gridsearchcv, hyperopt | 1,719,400,466 |
se_56134072 | stackoverflow | MACHINE_LEARNING | Random forest getting 100% score since feature selection | Random forest getting 100% score since feature selection
I have been working on a dataset. I have followed this process to predict churn:
Encoded and standardised data
Run random forest
Got a model score of 0.63
Analysed feature importances
Ran model for reduced feature set
Got a model score of 1.0 in the revised mod... | https://stackoverflow.com/questions/56134072/random-forest-getting-100-score-since-feature-selection | python, machine-learning, scikit-learn | 1,557,848,201 |
se_77674168 | stackoverflow | MACHINE_LEARNING | installation old version of scikitlearn | installation old version of scikitlearn
how i can install old version of scikit-learn ?
i need use boston house price data set and this data set didnt work on new version of scilitlearn
beacuse this message:
load_boston` has been removed from scikit-learn since version 1.2.
The Boston housing prices dataset has an et... | https://stackoverflow.com/questions/77674168/installation-old-version-of-scikitlearn | python, scikit-learn | 1,702,811,600 |
se_42562146 | stackoverflow | MACHINE_LEARNING | Classification report with Nested Cross Validation in SKlearn (Average/Individual values) | Classification report with Nested Cross Validation in SKlearn (Average/Individual values)
Is it possible to get classification report from cross_val_score through some workaround? I'm using nested cross-validation and I can get various scores here for a model, however, I would like to see the classification report of ... | https://stackoverflow.com/questions/42562146/classification-report-with-nested-cross-validation-in-sklearn-average-individua | python, machine-learning, scikit-learn, classification, cross-validation | 1,488,476,022 |
se_47917287 | stackoverflow | MACHINE_LEARNING | How to hand-engineer features of TfidfVectorizer in Scikit-learn? | How to hand-engineer features of TfidfVectorizer in Scikit-learn?
I am trying to cluster documents by keywords. I'm using the following code to make a tdidf-matrix:
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(max_df=.8, max_features=1000,
... | https://stackoverflow.com/questions/47917287/how-to-hand-engineer-features-of-tfidfvectorizer-in-scikit-learn | python, scikit-learn, nlp | 1,513,825,085 |
se_64115084 | stackoverflow | MACHINE_LEARNING | Are predictions on scikit-learn models thread-safe? | Are predictions on scikit-learn models thread-safe?
Given some classifier (SVC/Forest/NN/whatever) is it safe to call .predict on the same instance concurrently from different threads?
From a distant point of view, my guess is they do not mutate any internal state. But I did not find anything in the docs about it.
Her... | https://stackoverflow.com/questions/64115084/are-predictions-on-scikit-learn-models-thread-safe | python, scikit-learn, thread-safety, predict | 1,601,365,581 |
se_53633838 | stackoverflow | MACHINE_LEARNING | Resolving differences between Keras and scikit-learn for simple fully-connected neural network | Resolving differences between Keras and scikit-learn for simple fully-connected neural network
I have built a fully-connected neural network in both scikit-learn (v 0.20.0) and Keras (v 2.2.4) with TensorFlow backend (v 1.12.0). There are 10 units in the single hidden layer. In both cases I choose the training and t... | https://stackoverflow.com/questions/53633838/resolving-differences-between-keras-and-scikit-learn-for-simple-fully-connected | python, tensorflow, scikit-learn, keras | 1,544,017,705 |
se_61453581 | stackoverflow | MACHINE_LEARNING | Can't import MinMaxScaler "from sklearn.preprocessing import MinMaxScaler" | Can't import MinMaxScaler "from sklearn.preprocessing import MinMaxScaler"
I run into the following error when running my script. Could someone please let me know what the issue could be? I have installed Sklearn on my system, and using "pip list" I can see 'sklearn 0.0" is present.
C:\Users\User\AppDat... | https://stackoverflow.com/questions/61453581/cant-import-minmaxscaler-from-sklearn-preprocessing-import-minmaxscaler | python, scikit-learn | 1,587,972,098 |
se_79657196 | stackoverflow | MACHINE_LEARNING | Why does sklearn GaussianMixtures spawn a cmd window when run from IDLE? | Why does sklearn GaussianMixtures spawn a cmd window when run from IDLE?
When I run this code using IDLE in windows, it spawns a temporary cmd window that disappears shortly thereafter.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture
# Step 1: Generate random data
np.ran... | https://stackoverflow.com/questions/79657196/why-does-sklearn-gaussianmixtures-spawn-a-cmd-window-when-run-from-idle | scikit-learn, python-idle, python-3.12, gaussian-mixture-model | 1,749,316,960 |
se_36057245 | stackoverflow | MACHINE_LEARNING | Relationship of two time series using Scikit or Pandas | Relationship of two time series using Scikit or Pandas
Say I have two hypothetical time series data one for rainfall and the other is ocean surface temperature.
Rainfall Time Series:
2001-12-31 25 mm
2002-12-31 50 mm
2003-12-31 75 mm
2004-12-31 50 mm
2005-12-31 25 mm
2006-12-31 10 mm
2007-12-31 6 ... | https://stackoverflow.com/questions/36057245/relationship-of-two-time-series-using-scikit-or-pandas | python, pandas, scikit-learn, time-series | 1,458,209,040 |
se_23030284 | stackoverflow | MACHINE_LEARNING | Content based recommender system with sklearn or numpy | Content based recommender system with sklearn or numpy
I am trying to build a content-based recommender system in python/pandas/numpy/sklearn.
Here are the matrix involved and their size:
X: n_customers * n_features (contains the features of each customer)
Y: n_customers *n_products (contains the scores given by ea... | https://stackoverflow.com/questions/23030284/content-based-recommender-system-with-sklearn-or-numpy | python, numpy, machine-learning, scikit-learn, recommendation-engine | 1,397,305,162 |
se_62730016 | stackoverflow | MACHINE_LEARNING | Python: 'StandardScaler' object has no attribute '_validate_data' | Python: 'StandardScaler' object has no attribute '_validate_data'
I recently updated my sklearn. However, since the upgrade I'm getting the error AttributeError: 'StandardScaler' object has no attribute '_validate_data'.
The following is a snippet of the code:
Xs = pd.DataFrame([[10,20], [20,30], [30,4... | https://stackoverflow.com/questions/62730016/python-standardscaler-object-has-no-attribute-validate-data | python, pandas, scikit-learn, standardization | 1,593,871,011 |
se_37685412 | stackoverflow | MACHINE_LEARNING | Avoid scaling binary columns in sci-kit learn StandsardScaler | Avoid scaling binary columns in sci-kit learn StandsardScaler
I'm building a linear regression model in sci-kit learn, and am scaling the inputs as a preprocessing step in a sci-kit learn Pipeline. Is there any way I can avoid scaling binary columns? What's happening is that these columns are being scaled with every o... | https://stackoverflow.com/questions/37685412/avoid-scaling-binary-columns-in-sci-kit-learn-standsardscaler | python, scikit-learn, multi-dimensional-scaling | 1,465,319,698 |
se_69083477 | stackoverflow | MACHINE_LEARNING | Using Ordinal Encoding with Pipeline when there is new data in test set | Using Ordinal Encoding with Pipeline when there is new data in test set
Trying to use pipeline of One-Hot/Ordinal encoding with different models using cross_val_score. When there is unseen data in test set I handle it like this for OHE:
ohe = make_column_transformer((OneHotEncoder(handle_unknown='ignore'), cate_cols),... | https://stackoverflow.com/questions/69083477/using-ordinal-encoding-with-pipeline-when-there-is-new-data-in-test-set | python, scikit-learn | 1,630,997,020 |
se_70965317 | stackoverflow | MACHINE_LEARNING | How to fit column wise ordinal encoding | How to fit column wise ordinal encoding
I have a dataframe like as shown below
tdf = pd.DataFrame({'grade': np.random.choice(list('AAAD'),size=(5)),
'dash': np.random.choice(list('PPPS'),size=(5)),
'dumeel': np.random.choice(list('QWRR'),size=(5)),
'dumma': np.r... | https://stackoverflow.com/questions/70965317/how-to-fit-column-wise-ordinal-encoding | python, pandas, machine-learning, scikit-learn, deep-learning | 1,643,856,067 |
se_65230811 | stackoverflow | MACHINE_LEARNING | Correct compute of equal error rate value | Correct compute of equal error rate value
My work is to classifiy users' legitimate data from importers' data. I used binary SVM classifier to output probabilites of the two classes. Then used the Roc curve from sklearn to return TPR and FPR vectors over different thresholds as follows:
classifier = SVC(kernel='rbf', ... | https://stackoverflow.com/questions/65230811/correct-compute-of-equal-error-rate-value | python, scikit-learn, svm | 1,607,588,367 |
se_36107820 | stackoverflow | MACHINE_LEARNING | How to tune parameters in Random Forest, using Scikit Learn? | How to tune parameters in Random Forest, using Scikit Learn?
class sklearn.ensemble.RandomForestClassifier(n_estimators=10,
criterion='gini',
max_depth=None,
min_samples_split=2,
... | https://stackoverflow.com/questions/36107820/how-to-tune-parameters-in-random-forest-using-scikit-learn | python, parameters, machine-learning, scikit-learn, random-forest | 1,458,425,448 |
se_38015181 | stackoverflow | MACHINE_LEARNING | Accuracy Score ValueError: Can't Handle mix of binary and continuous target | Accuracy Score ValueError: Can't Handle mix of binary and continuous target
I'm using linear_model.LinearRegression from scikit-learn as a predictive model. It works and it's perfect. I have a problem to evaluate the predicted results using the accuracy_score metric.
This is my true Data :
array([1, 1, 0, 0, 0, 0,... | https://stackoverflow.com/questions/38015181/accuracy-score-valueerror-cant-handle-mix-of-binary-and-continuous-target | python, machine-learning, scikit-learn, linear-regression, prediction | 1,466,776,637 |
se_29095769 | stackoverflow | MACHINE_LEARNING | Sklearn-GMM on large datasets | Sklearn-GMM on large datasets
I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set.
Can I use GMM.fit() (sklearn.mixture.GMM) repeatedly on mini batch of data ?? | https://stackoverflow.com/questions/29095769/sklearn-gmm-on-large-datasets | python, scikit-learn, bigdata, mixture-model | 1,426,585,362 |
se_41567895 | stackoverflow | MACHINE_LEARNING | Will scikit-learn utilize GPU? | Will scikit-learn utilize GPU?
Reading implementation of scikit-learn in TensorFlow: http://learningtensorflow.com/lesson6/ and scikit-learn: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html I'm struggling to decide which implementation to use.
scikit-learn is installed as part of the tenso... | https://stackoverflow.com/questions/41567895/will-scikit-learn-utilize-gpu | python, tensorflow, scikit-learn, k-means, neuraxle | 1,484,048,243 |
se_54945196 | stackoverflow | MACHINE_LEARNING | sklearn stratified k-fold CV with linear model like ElasticNetCV | sklearn stratified k-fold CV with linear model like ElasticNetCV
using cross validation (CV) with sklearn is quite easy and straight-forward. But the default implementation when setting cv=5 in a linear CV model, like ElasticNetCV or LassoCV is a KFold CV. For various reasons I'd like to use a StratifiedKFold. From th... | https://stackoverflow.com/questions/54945196/sklearn-stratified-k-fold-cv-with-linear-model-like-elasticnetcv | python, scikit-learn, regression, cross-validation | 1,551,445,194 |
se_56496708 | stackoverflow | MACHINE_LEARNING | F1 Score: ValueError: Classification metrics can't handle a mix of multiclass and multilabel-indicator targets | F1 Score: ValueError: Classification metrics can't handle a mix of multiclass and multilabel-indicator targets
I have multi-class labeled text classification problem with 2000 different labels and I do classification using LSTM.
Label Encoder of target variable
LSTM layer with Embedd Layer
Error metric is F2 scor... | https://stackoverflow.com/questions/56496708/f1-score-valueerror-classification-metrics-cant-handle-a-mix-of-multiclass-an | python, machine-learning, scikit-learn, data-science, lstm | 1,559,919,337 |
se_79628711 | stackoverflow | MACHINE_LEARNING | Grid search scikeras error : 'super' object has no attribute '__sklearn_tags__' | Grid search scikeras error : 'super' object has no attribute '__sklearn_tags__'
I am trying to run a gridsearch in my pycharm jupyter notebook.
Getting 'super' object has no attribute '__sklearn_tags__' error. This question was asked for RandomizedSearch previously, but none of the answers seem to wo... | https://stackoverflow.com/questions/79628711/grid-search-scikeras-error-super-object-has-no-attribute-sklearn-tags | python, tensorflow, keras, scikit-learn, pycharm | 1,747,656,504 |
se_48413264 | stackoverflow | MACHINE_LEARNING | M nearest points to centroid in K-Means clustering | M nearest points to centroid in K-Means clustering
I have implemented a function to find the nearest data point to each centroid calculated after running the K-Means clustering algorithm. I wanted to know if there's a sklearn function that allows me to find the M nearest points to each of the centroids. | https://stackoverflow.com/questions/48413264/m-nearest-points-to-centroid-in-k-means-clustering | python, scikit-learn, cluster-analysis, k-means, centroid | 1,516,755,415 |
se_75044832 | stackoverflow | MACHINE_LEARNING | How do the splits in a decision tree classifier work? | How do the splits in a decision tree classifier work?
I would like to know how the splits in the decision tree look like.
More specifically, I am wondering if they can be "relative". For example can the criterion be something like:
if x > y ..
or does it have to be some absolute value, as in:
if x > 0.5 ..
I don't ... | https://stackoverflow.com/questions/75044832/how-do-the-splits-in-a-decision-tree-classifier-work | scikit-learn, random-forest, decision-tree | 1,673,139,358 |
se_29611842 | stackoverflow | MACHINE_LEARNING | scikit-learn kernel PCA explained variance | scikit-learn kernel PCA explained variance
I have been using the normal PCA from scikit-learn and get the variance ratios for each principal component without any issues.
pca = sklearn.decomposition.PCA(n_components=3)
pca_transform = pca.fit_transform(feature_vec)
var_values = pca.explained_variance_ratio_
I want ... | https://stackoverflow.com/questions/29611842/scikit-learn-kernel-pca-explained-variance | python, scikit-learn | 1,428,947,656 |
se_46015426 | stackoverflow | MACHINE_LEARNING | How can I parallelize fitting a gradient boosting model with sklearn? | How can I parallelize fitting a gradient boosting model with sklearn?
Relatively new to model building with sklearn. I know cross validation can be parallelized via the n_jobs parameter, but if I'm not using CV, how can I utilize my available cores to speed up model fitting? | https://stackoverflow.com/questions/46015426/how-can-i-parallelize-fitting-a-gradient-boosting-model-with-sklearn | python, machine-learning, scikit-learn | 1,504,366,711 |
se_47521872 | stackoverflow | MACHINE_LEARNING | Accessing Lasso Regression coefficients after fitting | Accessing Lasso Regression coefficients after fitting
I'm trying to Lasso Regression after having optimal value of Lambda and now the problem is , I want to get the coefficients (weight vector) since I want to compare them with weights of Ridge regression.
lasso = Lasso(alpha=optimal_lmbda, fit_intercept=True, random... | https://stackoverflow.com/questions/47521872/accessing-lasso-regression-coefficients-after-fitting | python, machine-learning, scikit-learn, regression, linear-regression | 1,511,827,518 |
se_79290968 | stackoverflow | MACHINE_LEARNING | 'super' object has no attribute '__sklearn_tags__' | 'super' object has no attribute '__sklearn_tags__'
I am encountering an AttributeError while fitting an XGBRegressor using RandomizedSearchCV from Scikit-learn. The error message states:
'super' object has no attribute '__sklearn_tags__'.
This occurs when I invoke the fit method on the RandomizedSearc... | https://stackoverflow.com/questions/79290968/super-object-has-no-attribute-sklearn-tags | python, machine-learning, scikit-learn, xgboost | 1,734,522,352 |
se_76613438 | stackoverflow | MACHINE_LEARNING | What is responsible for this TypeError: DataUndersampler.transform() missing 1 required positional argument: 'y'? | What is responsible for this TypeError: DataUndersampler.transform() missing 1 required positional argument: 'y'?
This is a custom support vectorbased data undersampler answer from my previous question.
The main idea is to undersample the majority class in an informed way, by fitting an SVC to the data, find t... | https://stackoverflow.com/questions/76613438/what-is-responsible-for-this-typeerror-dataundersampler-transform-missing-1-r | python, machine-learning, scikit-learn, classification | 1,688,479,384 |
se_49538377 | stackoverflow | MACHINE_LEARNING | Change in random state value changes the model accuracy | Change in random state value changes the model accuracy
While testing my linear regression model, I find that changing the random_state parameter in train_test_split changes the model accuracy.
To elaborate, my train test split is as follows:
boston_data = load_boston()
X = pd.DataFrame(boston_data.data, columns=bos... | https://stackoverflow.com/questions/49538377/change-in-random-state-value-changes-the-model-accuracy | python, scikit-learn, cross-validation, train-test-split | 1,522,250,558 |
se_40025406 | stackoverflow | MACHINE_LEARNING | Inherit from scikit-learn's LassoCV model | Inherit from scikit-learn's LassoCV model
I tried to extend scikit-learn's RidgeCV model using inheritance:
from sklearn.linear_model import RidgeCV, LassoCV
class Extended(RidgeCV):
def __init__(self, *args, **kwargs):
super(Extended, self).__init__(*args, **kwargs)
def example(self):
p... | https://stackoverflow.com/questions/40025406/inherit-from-scikit-learns-lassocv-model | python, scikit-learn | 1,476,373,212 |
se_57835219 | stackoverflow | MACHINE_LEARNING | Different answers with imputation of missing values | Different answers with imputation of missing values
I have made a classifier using Logistic Regression and for testing it I used the breast cancer dataset available at:
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
This dataset contains missing values so I have changed those values wit... | https://stackoverflow.com/questions/57835219/different-answers-with-imputation-of-missing-values | python, pandas, numpy, scikit-learn | 1,567,870,980 |
se_59254662 | stackoverflow | MACHINE_LEARNING | sklearn ColumnTransformer with MultilabelBinarizer | sklearn ColumnTransformer with MultilabelBinarizer
I wonder if it is possible to use a MultilabelBinarizer within a ColumnTransformer.
I have a toy pandas dataframe like:
df = pd.DataFrame({"id":[1,2,3],
"text": ["some text", "some other text", "yet another text"],
"label": [["white", "cat"], ["black", "cat"], ["b... | https://stackoverflow.com/questions/59254662/sklearn-columntransformer-with-multilabelbinarizer | python, python-3.x, scikit-learn, pipeline | 1,575,916,150 |
se_52933862 | stackoverflow | MACHINE_LEARNING | Sklearn or Pandas, impute missing values with simple linear regression | Sklearn or Pandas, impute missing values with simple linear regression
I have time series data, and I want to impute the missing data. I cant use mean of the column because I think it's not good for time series data.
So I want simple linear regression to impute it
Day, Price
1, NaN
2, NaN
3, 1800
4, 1900
5, NaN
... | https://stackoverflow.com/questions/52933862/sklearn-or-pandas-impute-missing-values-with-simple-linear-regression | python, pandas, scikit-learn, data-mining | 1,540,225,790 |
se_79601928 | stackoverflow | MACHINE_LEARNING | do i need to scale the rf model while creating voting ensemble model? | do i need to scale the rf model while creating voting ensemble model?
I'm building a classification model for sleep disorders using Voting Ensemble and I have three base models: Logistic Regression, Random Forest and SVM.
Now I want to combine these models using a Voting Ensemble with soft voting.I don't really have a... | https://stackoverflow.com/questions/79601928/do-i-need-to-scale-the-rf-model-while-creating-voting-ensemble-model | python, machine-learning, scikit-learn, random-forest, ensemble-learning | 1,746,105,731 |
se_79603555 | stackoverflow | MACHINE_LEARNING | How to set a fixed random state in RandomizedSearchCV? | How to set a fixed random state in RandomizedSearchCV?
I'm using RandomizedSearchCV with RandomForestClassifier in scikit-learn. I want to make sure my results are reproducible across runs. Where should I set the random_state—in the classifier, in RandomizedSearchCV, or both?
Example code:
from sklearn.ensemble import... | https://stackoverflow.com/questions/79603555/how-to-set-a-fixed-random-state-in-randomizedsearchcv | python, scikit-learn, data-science | 1,746,195,585 |
se_79602705 | stackoverflow | MACHINE_LEARNING | Why is DecisionTree using same feature and same condition twice | Why is DecisionTree using same feature and same condition twice
When trying to fit scikit-learn DecisionTreeClassifier on my data, I am observing some weird behavior.
x[54] (a boolan feature) is used to break the 19 samples into 2 and 17 on top left node. Then again, the same feature with exact same condition appears ... | https://stackoverflow.com/questions/79602705/why-is-decisiontree-using-same-feature-and-same-condition-twice | python, machine-learning, scikit-learn, classification, decision-tree | 1,746,148,450 |
se_75472472 | stackoverflow | MACHINE_LEARNING | Feature Importances for categorical features after one hot encoding? | Feature Importances for categorical features after one hot encoding?
We need to encode a categorical feature with multiple categories, the resulting one-hot encoded feature will have importance scores separately for each category. If we want to combine these importances into a single feature importance for the origina... | https://stackoverflow.com/questions/75472472/feature-importances-for-categorical-features-after-one-hot-encoding | machine-learning, scikit-learn, feature-selection | 1,676,551,445 |
se_55325114 | stackoverflow | MACHINE_LEARNING | How to express Root Mean Squared Error as a percentage? | How to express Root Mean Squared Error as a percentage?
I want to compare the result of my prediction with that of another person's prediction. In the article, the author says 'The relative percentage of root mean square (RMS%) was used to evaluate the performance'. This is what I want to compare my prediction to.
C... | https://stackoverflow.com/questions/55325114/how-to-express-root-mean-squared-error-as-a-percentage | python, machine-learning, scikit-learn, mean-square-error | 1,553,439,602 |
se_69675761 | stackoverflow | MACHINE_LEARNING | Scikit-learn: NameError: name 'load_iris' is not defined | Scikit-learn: NameError: name 'load_iris' is not defined
I got the error message of scikit-learn: iris database = load_iris () and after I get the error :NameError: name 'load_iris' is not defined
What could be the problem? Help me please!
I use :
Phyton 3.9.5
Anaconda 4.10.1
Scikit-learn 2.2.0 | https://stackoverflow.com/questions/69675761/scikit-learn-nameerror-name-load-iris-is-not-defined | python, scikit-learn | 1,634,900,640 |
se_79586660 | stackoverflow | MACHINE_LEARNING | Linear regression prediction does not display properly | Linear regression prediction does not display properly
I want to make 2 different linear regressions for 2 diferent plots, but on the same figure. I have a problem with the y1_pred because it does not go for all the y axis where are scatters.
model1 = LinearRegression().fit(x, y)
intercept1 = model1.intercept_
slope1 ... | https://stackoverflow.com/questions/79586660/linear-regression-prediction-does-not-display-properly | python, matplotlib, scikit-learn | 1,745,330,758 |
se_38646040 | stackoverflow | MACHINE_LEARNING | AttributeError: LinearRegression object has no attribute 'coef_' | AttributeError: LinearRegression object has no attribute 'coef_'
I've been attempting to fit this data by a Linear Regression, following a tutorial on bigdataexaminer. Everything was working fine up until this point. I imported LinearRegression from sklearn, and printed the number of coefficients just fine. Th... | https://stackoverflow.com/questions/38646040/attributeerror-linearregression-object-has-no-attribute-coef | python, python-3.x, scikit-learn, linear-regression, attributeerror | 1,469,737,342 |
se_59640880 | stackoverflow | MACHINE_LEARNING | How to determine the positive class in roc_auc_score? | How to determine the positive class in roc_auc_score?
I want to use roc_auc_score to evaluate the performance of the classifier, but I'm not sure what is the right parameters to give it.
This is a description of this function in the documentation: documentation.
As you can see, it needs y_score, which is the probabi... | https://stackoverflow.com/questions/59640880/how-to-determine-the-positive-class-in-roc-auc-score | machine-learning, scikit-learn, svm, auc | 1,578,466,881 |
se_69476352 | stackoverflow | MACHINE_LEARNING | sklearn MinMaxScaler() with groupby pandas | sklearn MinMaxScaler() with groupby pandas
I have two features rank and ratings for different product IDs under different categories scraped from an ecommerce website on different dates.
sample dataframe available here:
import pandas as pd
import numpy as np
import warnings; warnings.simplefilter('ignore')
from sklear... | https://stackoverflow.com/questions/69476352/sklearn-minmaxscaler-with-groupby-pandas | python, pandas, scikit-learn | 1,633,587,996 |
se_52648383 | stackoverflow | MACHINE_LEARNING | How to get coefficients and feature importances from MultiOutputRegressor? | How to get coefficients and feature importances from MultiOutputRegressor?
I am trying to perform a MultiOutput Regression using ElasticNet and Random Forests as follows:
from sklearn.ensemble import RandomForestRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.linear_model import ElasticNet... | https://stackoverflow.com/questions/52648383/how-to-get-coefficients-and-feature-importances-from-multioutputregressor | python, machine-learning, scikit-learn | 1,538,660,801 |
se_79586934 | stackoverflow | MACHINE_LEARNING | Python Sklearn.Model_Selection giving error numpy.dtype size changed | Python Sklearn.Model_Selection giving error numpy.dtype size changed
I have a train test split code
from sklearn.model_selection import train_test_split
train_df, test_df = train_test_split(new_cleaned_df, test_size=0.05, random_state=42, shuffle=True)
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index... | https://stackoverflow.com/questions/79586934/python-sklearn-model-selection-giving-error-numpy-dtype-size-changed | python, numpy, scikit-learn | 1,745,337,308 |
se_79227212 | stackoverflow | MACHINE_LEARNING | Polynomial regression with 2D array | Polynomial regression with 2D array
I have two 2D datasets Xand Y, which respectively represent experimental and theoretical data. In the two cases, each row corresponds to a sample (one physical configuration), and each column corresponds to a measurement done by a sensor at one particular location (feature).
These t... | https://stackoverflow.com/questions/79227212/polynomial-regression-with-2d-array | python, scikit-learn, regression, polynomials | 1,732,633,020 |
se_79418004 | stackoverflow | MACHINE_LEARNING | How to force all terms in 1-D Gaussian mixture model to have the same mean? | How to force all terms in 1-D Gaussian mixture model to have the same mean?
I have a one-dimensional set of data points, of which I want to parameterise the probability density. I have reasons to believe that a Gaussian mixture model would be a good way to accomplish this, so I'm trying to use scikit-learn's GaussianM... | https://stackoverflow.com/questions/79418004/how-to-force-all-terms-in-1-d-gaussian-mixture-model-to-have-the-same-mean | python, scikit-learn | 1,738,847,045 |
se_79515542 | stackoverflow | MACHINE_LEARNING | Length of features is not equal to the length of SHAP Values | Length of features is not equal to the length of SHAP Values
Im running a random forest model and to get some feature importance and Im trying to run a SHAP analysis. The problem is that every time I try to plot the shap values, I keep getting this error:
DimensionError: Length of features is not equal to the length o... | https://stackoverflow.com/questions/79515542/length-of-features-is-not-equal-to-the-length-of-shap-values | python, machine-learning, scikit-learn, random-forest, shap | 1,742,239,005 |
se_34143829 | stackoverflow | MACHINE_LEARNING | Sklearn How to Save a Model Created From a Pipeline and GridSearchCV Using Joblib or Pickle? | Sklearn How to Save a Model Created From a Pipeline and GridSearchCV Using Joblib or Pickle?
After identifying the best parameters using a pipeline and GridSearchCV, how do I pickle/joblib this process to re-use later? I see how to do this when it's a single classifier...
import joblib
joblib.dump(clf, 'filename.pkl')... | https://stackoverflow.com/questions/34143829/sklearn-how-to-save-a-model-created-from-a-pipeline-and-gridsearchcv-using-jobli | python, scikit-learn, file-io, pickle, gridsearchcv | 1,449,524,783 |
se_79583640 | stackoverflow | MACHINE_LEARNING | Keras SKLearnClassifier wrapper can't fit MNIST data | Keras SKLearnClassifier wrapper can't fit MNIST data
I'm trying to use the SKLearnClassifier Keras wrapper to do some grid searching and cross validation using the sklearn library but I'm unable to get the model to work properly.
def build_model(X, y, n_neurons: List[str], learning_rate: float):
model = keras.... | https://stackoverflow.com/questions/79583640/keras-sklearnclassifier-wrapper-cant-fit-mnist-data | python, keras, scikit-learn, mnist | 1,745,173,911 |
se_70719940 | stackoverflow | MACHINE_LEARNING | How to get bag of words and term frequency in text format using Sklearn? | How to get bag of words and term frequency in text format using Sklearn?
I would like to print out the list of words (i.e., bag of words) for each document in a coprus and their respective term frequency (in text format), using Sklearn's CountVectorizer. How could I achieve that?
Here is my code:
from sklearn.feature_... | https://stackoverflow.com/questions/70719940/how-to-get-bag-of-words-and-term-frequency-in-text-format-using-sklearn | python, scikit-learn, nlp, corpus, countvectorizer | 1,642,235,584 |
se_79407078 | stackoverflow | MACHINE_LEARNING | GaussianProcessRegressor object in scikit-learn: select fixed hyperparameters, cannot reproduce optimised kernel | GaussianProcessRegressor object in scikit-learn: select fixed hyperparameters, cannot reproduce optimised kernel
I am trying to understand the GaussianProcessRegressor object in scikit-learn alas, unsuccessfully.
Considering the example in the documentation Example with noisy targets, which I copy below for convenienc... | https://stackoverflow.com/questions/79407078/gaussianprocessregressor-object-in-scikit-learn-select-fixed-hyperparameters-c | machine-learning, scikit-learn, gaussian-process | 1,738,520,249 |
se_30843011 | stackoverflow | MACHINE_LEARNING | Save and reuse TfidfVectorizer in scikit learn | Save and reuse TfidfVectorizer in scikit learn
I am using TfidfVectorizer in scikit learn to create a matrix from text data. Now I need to save this object to reuse it later. I tried to use pickle, but it gave the following error.
loc=open('vectorizer.obj','w')
pickle.dump(self.vectorizer,loc)
*** TypeError: can't pic... | https://stackoverflow.com/questions/30843011/save-and-reuse-tfidfvectorizer-in-scikit-learn | python, scikit-learn, file-io, nlp, pickle | 1,434,364,538 |
se_79576028 | stackoverflow | MACHINE_LEARNING | Confirm understanding of decision_function in Isolation Forest | Confirm understanding of decision_function in Isolation Forest
I am looking to better understand sklearn IsolationForest decision_function. My understanding from this previous stack overflow post, What is the difference between decision function and score_samples in isolation_forest in SKLearn, if the metric is close... | https://stackoverflow.com/questions/79576028/confirm-understanding-of-decision-function-in-isolation-forest | python, machine-learning, scikit-learn, anomaly-detection, isolation-forest | 1,744,751,255 |
se_79504108 | stackoverflow | MACHINE_LEARNING | How is each tree within DecisionTreeClassifier calculating probability of a class? | How is each tree within DecisionTreeClassifier calculating probability of a class?
According to the sklearn docs, if you apply predict_proba to DecisionTreeClassifier:
The predicted class probability is the fraction of samples of the same
class in a leaf.
Let's say that the rows where class = 1 in my training datase... | https://stackoverflow.com/questions/79504108/how-is-each-tree-within-decisiontreeclassifier-calculating-probability-of-a-clas | scikit-learn, classification, decision-tree | 1,741,794,197 |
se_79577490 | stackoverflow | MACHINE_LEARNING | How to fit scaler for different subsets of rows depending on group variable and include it in a Pipeline? | How to fit scaler for different subsets of rows depending on group variable and include it in a Pipeline?
I have a data set like the following and want to scale the data using any of the scalers in sklearn.preprocessing.
Is there an easy way to fit this scaler not over the whole data set, but per group? My current sol... | https://stackoverflow.com/questions/79577490/how-to-fit-scaler-for-different-subsets-of-rows-depending-on-group-variable-and | python, pandas, scikit-learn, data-transform, scikit-learn-pipeline | 1,744,815,535 |
se_79570774 | stackoverflow | MACHINE_LEARNING | reg.predict is telling me I am not providing an array | reg.predict is telling me I am not providing an array
It seems I have an issue with an array that I thought I coded correctly. When I ask for reg.score or reg.coef_ the code works great, but when I try to predict it throws an error that is saying it is not 2 dimensional or an array. Can somebody tell me what I did wro... | https://stackoverflow.com/questions/79570774/reg-predict-is-telling-me-i-am-not-providing-an-array | python, scikit-learn | 1,744,480,371 |
se_74281988 | stackoverflow | MACHINE_LEARNING | How to input a matrix and receive value (or array of values) using machine learning in Python? | How to input a matrix and receive value (or array of values) using machine learning in Python?
I have a bunch of binary 8x15 matrixes and I want to determine which index (row and column) of the matrix breaks the pattern.
Say I have the following matrix (simplified to 5x5 for ease, because the problem remains the same)... | https://stackoverflow.com/questions/74281988/how-to-input-a-matrix-and-receive-value-or-array-of-values-using-machine-learn | python, machine-learning, matrix, scikit-learn | 1,667,338,138 |
se_79575941 | stackoverflow | MACHINE_LEARNING | Why does RandomForestClassifier in scikit-learn predict even on all-NaN input? | Why does RandomForestClassifier in scikit-learn predict even on all-NaN input?
I am training a random forest classifier in python sklearn, see code below-
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(random_state=42)
rf.fit(X = df.drop("AP", axis =1), y = df["AP"].astype(int))
When... | https://stackoverflow.com/questions/79575941/why-does-randomforestclassifier-in-scikit-learn-predict-even-on-all-nan-input | python, machine-learning, scikit-learn, random-forest, missing-data | 1,744,746,839 |
se_61708970 | stackoverflow | MACHINE_LEARNING | What are Mutual_info_regression and Mutual_info_classif used for in scikit-learn? | What are Mutual_info_regression and Mutual_info_classif used for in scikit-learn?
I'm learning about feature selection of big datasets. I came across methods called Mutual_info_regression() and Mutual_info_classif().
They return a value for all the features. What does that value represent? | https://stackoverflow.com/questions/61708970/what-are-mutual-info-regression-and-mutual-info-classif-used-for-in-scikit-learn | python, machine-learning, scikit-learn | 1,589,098,165 |
se_53470097 | stackoverflow | MACHINE_LEARNING | how to apply mutual information on categorical features | how to apply mutual information on categorical features
I am using Scikit-learn to train a classification model. I have both discrete and continuous features in my training data.
I want to do feature selection using mutual information.
The features 1,2 and 3 are discrete. to this end, I try the code below :
mut... | https://stackoverflow.com/questions/53470097/how-to-apply-mutual-information-on-categorical-features | python, scikit-learn | 1,543,167,280 |
se_22562540 | stackoverflow | MACHINE_LEARNING | Pandas dataset into an array for modelling in Scikit-Learn | Pandas dataset into an array for modelling in Scikit-Learn
Can we run scikit-learn models on Pandas DataFrames or do we need to convert DataFrames into NumPy arrays? | https://stackoverflow.com/questions/22562540/pandas-dataset-into-an-array-for-modelling-in-scikit-learn | python, pandas, scikit-learn | 1,395,412,291 |
se_55108902 | stackoverflow | MACHINE_LEARNING | module 'numpy' has no attribute 'dtype' | module 'numpy' has no attribute 'dtype'
When importing sklearn datasets eg.
from sklearn.datasets import fetch_mldata
from sklearn.datasets import fetch_openml
I get the error
Traceback (most recent call last):
File "numbers.py", line 1, in <module>
from sklearn.datasets import fetch_openml
... | https://stackoverflow.com/questions/55108902/module-numpy-has-no-attribute-dtype | python, numpy, scikit-learn, python-3.5, python-import | 1,552,331,940 |
se_23939750 | stackoverflow | MACHINE_LEARNING | Understanding max_features parameter in RandomForestRegressor | Understanding max_features parameter in RandomForestRegressor
While constructing each tree in the random forest using bootstrapped samples, for each terminal node, we select m variables at random from p variables to find the best split (p is the total number of features in your data). My questions (for RandomForestReg... | https://stackoverflow.com/questions/23939750/understanding-max-features-parameter-in-randomforestregressor | machine-learning, scikit-learn, random-forest, feature-selection | 1,401,385,976 |
se_79454515 | stackoverflow | MACHINE_LEARNING | Specify minimum surface constraint for sklearn GPR prediction | Specify minimum surface constraint for sklearn GPR prediction
I'm trying to use sklearn.GaussianProccessRegression to fit data, then estimate new points using the regression. The predictions need to be positive (preferably above a specified minimum).
I'd like to use that minimum as a constraint in the solver. The back... | https://stackoverflow.com/questions/79454515/specify-minimum-surface-constraint-for-sklearn-gpr-prediction | optimization, scikit-learn, gaussian-process | 1,740,054,540 |
se_42302245 | stackoverflow | MACHINE_LEARNING | Stratified GroupShuffleSplit in Scikit-learn | Stratified GroupShuffleSplit in Scikit-learn
I would like to ask if it is possible to do "Stratified GroupShuffleSplit" in scikit-learn which is in other words a combination of GroupShuffleSplit and StratifiedShuffleSplit
Here is a sample of the code I am using:
cv=GroupShuffleSplit(n_splits=n_splits,test_size=test_... | https://stackoverflow.com/questions/42302245/stratified-groupshufflesplit-in-scikit-learn | python, scikit-learn, dataset, shuffle, cross-validation | 1,487,347,190 |
se_30384995 | stackoverflow | MACHINE_LEARNING | RandomForestClassfier.fit(): ValueError: could not convert string to float | RandomForestClassfier.fit(): ValueError: could not convert string to float
Given is a simple CSV file:
A,B,C
Hello,Hi,0
Hola,Bueno,1
Obviously the real dataset is far more complex than this, but this one reproduces the error. I'm attempting to build a random forest classifier for it, like so:
cols = ['A','B','C']
... | https://stackoverflow.com/questions/30384995/randomforestclassfier-fit-valueerror-could-not-convert-string-to-float | python, scikit-learn, random-forest | 1,432,245,075 |
se_65676129 | stackoverflow | MACHINE_LEARNING | Yocto recipe for scikit-learn | Yocto recipe for scikit-learn
I am trying to create a yocto recipe for scikit-learn package. It depends on scipy pacakge. I was able to successfully build the scipy package using : https://github.com/gpanders/meta-scipy.
When I run bitbake python3-scikit-learn, i am getting the below error:
ModuleNotFoundError: No mod... | https://stackoverflow.com/questions/65676129/yocto-recipe-for-scikit-learn | python, scikit-learn, scipy, yocto, bitbake | 1,610,408,860 |
se_28888336 | stackoverflow | MACHINE_LEARNING | error with the loading files in scikit | error with the loading files in scikit
I am new to scikit-learn and tried to follow the tutorial on the site :
http://scikit-learn.org/dev/tutorial/text_analytics/working_with_text_data.html
I downloaded the dataset, and used the load_files to load it but I keep getting error about the encoding. Below is how it is lo... | https://stackoverflow.com/questions/28888336/error-with-the-loading-files-in-scikit | scikit-learn | 1,425,591,788 |
se_78991033 | stackoverflow | MACHINE_LEARNING | How to solve ERROR: daal4py 2021.5.0 requires daal==2021.4.0, which is not installed? | How to solve ERROR: daal4py 2021.5.0 requires daal==2021.4.0, which is not installed?
I try to install sklearn package and getting the warning below:
> File D:\ANACONDA\envs\TenserFlow\lib\site-packages\scipy\sparse\linalg\_svdp.py:24
from ._propack import _spropack # type: ignore[attr-defined]
ImportE... | https://stackoverflow.com/questions/78991033/how-to-solve-error-daal4py-2021-5-0-requires-daal-2021-4-0-which-is-not-insta | python, scikit-learn | 1,726,504,727 |
se_79371534 | stackoverflow | MACHINE_LEARNING | Have only 2 classes but why am I getting ValueError: Target is multiclass but average = 'binary'. Please choose another average setting | Have only 2 classes but why am I getting ValueError: Target is multiclass but average = 'binary'. Please choose another average setting
I am trying build an employee churn prediction model using GridSearchCV on OneClassSVM. My code is as below:
from sklearn.svm import OneClassSVM
from sklearn.model_selection i... | https://stackoverflow.com/questions/79371534/have-only-2-classes-but-why-am-i-getting-valueerror-target-is-multiclass-but-av | python, scikit-learn, svm, gridsearchcv | 1,737,381,037 |
se_75089762 | stackoverflow | MACHINE_LEARNING | I am using RuleFit for binary classification; how do I interpret the rules? | I am using RuleFit for binary classification; how do I interpret the rules?
I am using RuleFit with a GradientBoostingClassifier to generate rules for a binary classification problem (health-dataset on Kaggle). When I print out the rules with RuleFit.get_rules(), it shows rule, type, coef, support, and importance. B... | https://stackoverflow.com/questions/75089762/i-am-using-rulefit-for-binary-classification-how-do-i-interpret-the-rules | machine-learning, scikit-learn | 1,673,476,953 |
se_61292464 | stackoverflow | MACHINE_LEARNING | Get confidence interval from sklearn linear regression in python | Get confidence interval from sklearn linear regression in python
I want to get a confidence interval of the result of a linear regression. I'm working with the boston house price dataset.
I've found this question:
How to calculate the 99% confidence interval for the slope in a linear regression model in python?
Howeve... | https://stackoverflow.com/questions/61292464/get-confidence-interval-from-sklearn-linear-regression-in-python | python, machine-learning, scikit-learn, statistics, linear-regression | 1,587,226,950 |
se_34052115 | stackoverflow | MACHINE_LEARNING | How to find the importance of the features for a logistic regression model? | How to find the importance of the features for a logistic regression model?
I have a binary prediction model trained by logistic regression algorithm. I want know which features (predictors) are more important for the decision of positive or negative class. I know there is coef_ parameter which comes from the scikit-l... | https://stackoverflow.com/questions/34052115/how-to-find-the-importance-of-the-features-for-a-logistic-regression-model | python, machine-learning, scikit-learn, logistic-regression, feature-selection | 1,449,087,081 |
se_18706863 | stackoverflow | MACHINE_LEARNING | Difference between TruncatedSVD and svds | Difference between TruncatedSVD and svds
I see that the documentation for both sklearn.decomposition.TruncatedSVD and scipy.sparse.linalg.svds mention that they both perform SVD for sparse matrices. What is the difference between them?
Thanks. | https://stackoverflow.com/questions/18706863/difference-between-truncatedsvd-and-svds | scipy, scikit-learn, svd | 1,378,760,083 |
se_79225855 | stackoverflow | MACHINE_LEARNING | Cannot import name '_check_array_key' from 'skfda._utils' in Python 3.12.7 | Cannot import name '_check_array_key' from 'skfda._utils' in Python 3.12.7
I am trying to use skfda library in my python code. I have already installed the package using `pip install scikit-fda, however, the error was a little bit strange for me. Can anyone explain why this keeps showing and how to fix... | https://stackoverflow.com/questions/79225855/cannot-import-name-check-array-key-from-skfda-utils-in-python-3-12-7 | python, scikit-learn | 1,732,609,147 |
se_61829228 | stackoverflow | MACHINE_LEARNING | Latest XGboost and Sklearn giving keyerror | Latest XGboost and Sklearn giving keyerror
xgb=XGBClassifier(objective="binary:logistic", n_estimators=100, random_state=42, eval_metric=["auc"])
xgb.fit(X_train, y_train)
---------------------------------------------------------------------------
KeyError Traceback (most recent call ... | https://stackoverflow.com/questions/61829228/latest-xgboost-and-sklearn-giving-keyerror | scikit-learn, xgboost | 1,589,564,722 |
se_9677603 | stackoverflow | MACHINE_LEARNING | Naive Bayes classifier | Naive Bayes classifier
I'm using scikit-learn for finding the Tf-idf weight of a document and then using the Naive
Bayesian classifier to classify the text. But the Tf-idf weight of all words in a documents are negative except a few. But as far as I know, negative values means unimportant terms. So is it necessary to... | https://stackoverflow.com/questions/9677603/naive-bayes-classifier | machine-learning, scikit-learn, bayesian | 1,331,605,724 |
se_69573659 | stackoverflow | MACHINE_LEARNING | Custom SVM Polynomial kernel | Custom SVM Polynomial kernel
I got asked as an assignment to develop a custom polynomial (degree = 3,4,5) kernel for SVM and compare its accuracy to the in-built poly kernel of sklearn (should be almost the same).
I tried to follow the polynomial kernel definition but my results seems not to be quite similar, this is ... | https://stackoverflow.com/questions/69573659/custom-svm-polynomial-kernel | python, machine-learning, scikit-learn, svm, polynomial-math | 1,634,226,368 |
se_79248908 | stackoverflow | MACHINE_LEARNING | HistGradientBoostingClassifier Using Categorical Variables | HistGradientBoostingClassifier Using Categorical Variables
I landed on the HistGradientBoostingClassifier because, according to the docs, it has native support for categorical variables without the need for one-hot encoding or label encoding. I tried to run categorical variables through the model but when I fit the mo... | https://stackoverflow.com/questions/79248908/histgradientboostingclassifier-using-categorical-variables | python, scikit-learn, classification, ensemble-learning | 1,733,253,421 |
se_65980952 | stackoverflow | MACHINE_LEARNING | Error: Could not install packages due to an OSError: [Errno 2] No such file or directory | Error: Could not install packages due to an OSError: [Errno 2] No such file or directory
I try to use pip to install sklearn, and I receive the following error message:
ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\Users\13434\AppData\Local\Packages\PythonSoftwareFounda... | https://stackoverflow.com/questions/65980952/error-could-not-install-packages-due-to-an-oserror-errno-2-no-such-file-or-d | python, scikit-learn, pip | 1,612,107,381 |
se_74485762 | stackoverflow | MACHINE_LEARNING | Scikit-Learn Linear Regression using Datetime Values and forecasting | Scikit-Learn Linear Regression using Datetime Values and forecasting
Below is a sample of the dataset.
row_id
datetime
energy
1
2008-03-01 00:00:00
1259.985563
2
2008-03-01 01:00:00
1095.541500
3
2008-03-01 02:00:00
1056.247500
4
2008-03-01 03:00:00
1034.742000
5
2008-03-01 04:00:00
1026.334500
The d... | https://stackoverflow.com/questions/74485762/scikit-learn-linear-regression-using-datetime-values-and-forecasting | python, pandas, datetime, scikit-learn, forecasting | 1,668,753,451 |
se_75119268 | stackoverflow | MACHINE_LEARNING | Can't get LogisticRegressionCV to converge for any other Cs then 1 | Can't get LogisticRegressionCV to converge for any other Cs then 1
I'm trying to build a logistic regression model with an array of hyperparameter values such as:
lambdas = [0.001, 0.01, 0.05, 0.1, 1., 100.]
However, the model won't converge unless i have Cs = 1.Here is my code:
X_train, X_test, y_train, y_test = ... | https://stackoverflow.com/questions/75119268/cant-get-logisticregressioncv-to-converge-for-any-other-cs-then-1 | scikit-learn, logistic-regression, cross-validation | 1,673,712,043 |
se_77701188 | stackoverflow | MACHINE_LEARNING | How to get features selected using Boruta in a Pandas Dataframe with headers | How to get features selected using Boruta in a Pandas Dataframe with headers
I have this boruta code, and I want to generate the results in pandas with columns included
model = RandomForestRegressor(n_estimators=100, max_depth=5, random_state=42)
# let's initialize Boruta
feat_selector = BorutaPy(
verbose=2,
... | https://stackoverflow.com/questions/77701188/how-to-get-features-selected-using-boruta-in-a-pandas-dataframe-with-headers | python, pandas, filter, scikit-learn, feature-selection | 1,703,204,982 |
se_39048407 | stackoverflow | MACHINE_LEARNING | Importing scikit-learn in ipython/Jupyter notebook | Importing scikit-learn in ipython/Jupyter notebook
I am able to import scikit-learn from python or ipython console (both python 2.7 and 3.5), but when I do import sklearn within an ipython notebook (again, either 2.7 or 3.5) I get the error message below. How can I fix this?
ImportError
... | https://stackoverflow.com/questions/39048407/importing-scikit-learn-in-ipython-jupyter-notebook | scikit-learn, jupyter | 1,471,645,079 |
se_79529836 | stackoverflow | MACHINE_LEARNING | TabPFN feature selection raises KeyError(f"None of [{key}] are in the [{axis_name}]") | TabPFN feature selection raises KeyError(f"None of [{key}] are in the [{axis_name}]")
I trained a tabPFN model, which I then tried applying a sequential feature selector for important feature selection. I've been getting this error
KeyError(f"None of [{key}] are in the [{axis_name}]")
on the following code:... | https://stackoverflow.com/questions/79529836/tabpfn-feature-selection-raises-keyerrorfnone-of-key-are-in-the-axis-nam | python, machine-learning, scikit-learn, feature-selection | 1,742,770,762 |
se_79528929 | stackoverflow | MACHINE_LEARNING | Why does SequentialFeatureSelector return at most "n_features_in_ - 1" predictors? | Why does SequentialFeatureSelector return at most "n_features_in_ - 1" predictors?
I have a training dataset with six features and I am using SequentialFeatureSelector to find an "optimal" subset of the features for a linear regression model. The following code returns three features, which I will call X1, X... | https://stackoverflow.com/questions/79528929/why-does-sequentialfeatureselector-return-at-most-n-features-in-1-predictor | python, machine-learning, scikit-learn, linear-regression, sequentialfeatureselector | 1,742,732,163 |
se_53035563 | stackoverflow | MACHINE_LEARNING | Sentiment Analysis, Naive Bayes Accuracy | Sentiment Analysis, Naive Bayes Accuracy
I'm trying to form a Naive Bayes Classifier script for sentiment classification of tweets. I'm pasting my whole code here, because I know I will get hell if I don't. So I basically I use NLTK's corpuses as training data, and then some tweets I scraped as test data. I pre-proces... | https://stackoverflow.com/questions/53035563/sentiment-analysis-naive-bayes-accuracy | python, scikit-learn, nltk, sentiment-analysis, naivebayes | 1,540,756,774 |
se_73006073 | stackoverflow | MACHINE_LEARNING | How to apply linear regression to subsets of a dataframe using groupby | How to apply linear regression to subsets of a dataframe using groupby
I want to apply linear regression and predict values to subsets of my original data by V1, V2, V3, V4, V5, and V6. Then I want to store dataframe with names: V1, V2, V3, V4, V5, V6, time, Predicted value. How to achieve it effificiently? What I hav... | https://stackoverflow.com/questions/73006073/how-to-apply-linear-regression-to-subsets-of-a-dataframe-using-groupby | python, pandas, scikit-learn, group-by, apply | 1,657,990,267 |
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