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vitaliset/$duplicated_descriptions_see_also_sklearn.py

Created September 18, 2022 04:12
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Some of the See also descriptions are different from one class/function to another. Sometimes it makes sense, at others it doesn't.
Raw
$duplicated_descriptions_see_also_sklearn.py
from collections import defaultdict
from subprocess import check_output
from datetime import date
import os
import re
import numpy as np
import pandas as pd
# You should run this file inside the scikit-learn folder.
directorys = [os.fsencode("sklearn")]
files = []
# Here we are just looping through the files and checking if it is a python
# file or not. Docstrings should be on python files to the best of my knowledge.
for directory in directorys:
for filename in os.scandir(directory):
filename_str = filename.path.decode("utf-8")
if not filename.is_file():
directorys.append(filename.path)
elif filename_str.endswith('.py') and not ('__init__' in filename_str):
files.append(filename_str)
descriptions_dict = defaultdict(list)
for filename in files:
with open(filename, encoding="utf8") as file:
data = file.read()
# Checking everyfile for occurrences of a see also section.
start_of_see_also_indexes = \
[m.start() for m in re.finditer('see also', data.lower())]
see_also = []
for start_of_see_also_index in start_of_see_also_indexes:
# The See also section should finish with an empty line, following
# numpydoc validation (sklearn's issue #21350).
see_also.append(data[start_of_see_also_index:].split('\n\n')[0])
for also in see_also:
# When we have a See also section, numpydoc validation asks us
# to follow this pattern: "<name of reference><space>:<space>".
# Note: the .* symbol in regex means any number of any characters.
starts = [m.start() for m in re.finditer(' .* : ', also)]
ends = [m.end() for m in re.finditer(' .* : ', also)]
for i in range(len(starts)-1):
(descriptions_dict[also[starts[i]:ends[i]].split(':')[0].strip()]
.append([also[ends[i]:starts[i+1]], filename]))
# For each reference in the See also section, we are saving:
# 1 - the name of function/class beeing referenced (key of dict),
# 2 - the text describing the function,
# 3 - the filepath so we can find it easily.
not_unique_descriptions_references = []
for reference, descrs in descriptions_dict.items():
if len(np.unique(np.array(descrs)[:,0])) != 1:
not_unique_descriptions_references.append(reference)
not_unique_dict = \
{key: descriptions_dict[key] for key in not_unique_descriptions_references}
final_dataframe = \
(
pd.DataFrame(sum(not_unique_dict.values(),[]),
sum([len(val)*[key] for key, val in not_unique_dict.items()],[]),
['description', 'filepath']
)
.reset_index()
.sort_values('index')
)
today = date.today().strftime("%d%b%Y")
current_commit = \
check_output("git rev-parse --short HEAD").decode("utf-8").split()[0]
final_dataframe.to_csv(today+'_commit'+current_commit+'.csv', index=False)
# You can see a specific commit by going to:
# https://github.com/scikit-learn/scikit-learn/commit/<commit_name>
# For instace:
# https://github.com/scikit-learn/scikit-learn/commit/2f8b8e7f1
Raw
18Sep2022_commit2f8b8e7f1.md

This file was created from the outputed csv from the .py file using this csv to markdown translator.

Unfortunately, some of the differences related to extra "\n" are not shown here. To see this you should run the python file and explore the final_dataframe.

index description filepath
AgglomerativeClustering Agglomerative clustering samples instead of features. sklearn\cluster_agglomerative.py
AgglomerativeClustering Recursively merges the pair of clusters that minimally increases a given linkage distance. sklearn\cluster_affinity_propagation.py
ClassifierChain A multi-label model that arranges binary classifiers into a chain. sklearn\multioutput.py
ClassifierChain Equivalent for classification. sklearn\multioutput.py
ComplementNB Complement Naive Bayes classifier. sklearn\naive_bayes.py
ComplementNB The Complement Naive Bayes classifier described in Rennie et al. (2003). sklearn\naive_bayes.py
ComplementNB Complement Naive Bayes classifier. sklearn\naive_bayes.py
ComplementNB Complement Naive Bayes classifier. sklearn\naive_bayes.py
ConfusionMatrixDisplay.from_estimator Plot the confusion matrix given an estimator, the data, and the label. sklearn\metrics_plot\confusion_matrix.py
ConfusionMatrixDisplay.from_estimator Plot the confusion matrix given an estimator, the data, and the label. sklearn\inspection_plot\decision_boundary.py
ConfusionMatrixDisplay.from_estimator Plot the confusion matrix given an estimator, the data, and the label. sklearn\metrics_classification.py
ExtraTreesClassifier An extra-trees classifier with random splits. sklearn\ensemble_forest.py
ExtraTreesClassifier An extra-trees classifier. sklearn\ensemble_forest.py
ExtraTreesRegressor An extra-trees regressor with random splits. sklearn\ensemble_forest.py
ExtraTreesRegressor An extra-trees regressor. sklearn\ensemble_forest.py
FeatureAgglomeration Similar to AgglomerativeClustering, but recursively merges features instead of samples. sklearn\cluster_affinity_propagation.py
FeatureAgglomeration Agglomerative clustering but for features instead of samples. sklearn\cluster_agglomerative.py
GaussianNB Gaussian Naive Bayes. sklearn\naive_bayes.py
GaussianNB Gaussian Naive Bayes (GaussianNB). sklearn\naive_bayes.py
GaussianNB Gaussian Naive Bayes. sklearn\naive_bayes.py
GradientBoostingRegressor Exact gradient boosting method that does not scale as good on datasets with a large number of samples. sklearn\ensemble_hist_gradient_boosting\gradient_boosting.py
GradientBoostingRegressor Gradient Boosting Classification Tree. sklearn\ensemble_weight_boosting.py
IncrementalPCA Incremental principal components analysis. sklearn\decomposition_truncated_svd.py
IncrementalPCA Incremental principal components analysis. sklearn\decomposition_sparse_pca.py
IncrementalPCA Incremental principal components analysis (IPCA). sklearn\decomposition_fastica.py
IncrementalPCA Incremental Principal Component Analysis. sklearn\decomposition_kernel_pca.py
KNeighborsClassifier Classifier implementing the k-nearest neighbors vote. sklearn\neighbors_unsupervised.py
KNeighborsClassifier Classifier based on the k-nearest neighbors. sklearn\neighbors_regression.py
KNeighborsClassifier Classifier implementing the k-nearest neighbors vote. sklearn\neighbors_regression.py
KNeighborsClassifier Classifier implementing the k-nearest neighbors vote. sklearn\neighbors_classification.py
KernelPCA Kernel Principal component analysis. sklearn\decomposition_truncated_svd.py
KernelPCA Kernel Principal Component Analysis. sklearn\decomposition_pca.py
KernelPCA Kernel Principal component analysis (KPCA). sklearn\decomposition_incremental_pca.py
KernelPCA Kernel Principal component analysis (KPCA). sklearn\decomposition_fastica.py
LabelBinarizer Binarizes labels in a one-vs-all fashion. sklearn\preprocessing_encoders.py
LabelBinarizer Binarize labels in a one-vs-all fashion. sklearn\preprocessing_function_transformer.py
Lars Least Angle Regression model. sklearn\linear_model_stochastic_gradient.py
Lars Least Angle Regression model a.k.a. LAR. sklearn\linear_model_omp.py
Lars Least Angle Regression model a.k.a. LAR. sklearn\linear_model_omp.py
Lars Least Angle Regression model a.k.a. LAR. sklearn\linear_model_least_angle.py
Lars Least Angle Regression model a.k.a. LAR. sklearn\linear_model_least_angle.py
Lasso The Lasso is a linear model that estimates sparse coefficients. sklearn\linear_model_coordinate_descent.py
Lasso The Lasso is a linear model that estimates sparse coefficients. sklearn\linear_model_coordinate_descent.py
Lasso The Lasso is a linear model that estimates sparse coefficients with l1 regularization. sklearn\linear_model_base.py
Lasso Linear Model trained with L1 prior as regularizer (aka the Lasso). sklearn\linear_model_least_angle.py
Lasso Linear Model trained with L1 prior as regularizer (aka the Lasso). sklearn\linear_model_least_angle.py
Lasso The Lasso is a linear model that estimates sparse coefficients with l1 regularization. sklearn\linear_model_quantile.py
Lasso Linear Model trained with L1 prior as regularizer. sklearn\linear_model_stochastic_gradient.py
Lasso Linear Model trained with L1 prior as regularizer (aka the Lasso). sklearn\linear_model_least_angle.py
Lasso Linear Model trained with L1 prior as regularizer (aka the Lasso). sklearn\linear_model_least_angle.py
LassoCV Lasso alpha parameter by cross-validation. sklearn\linear_model_coordinate_descent.py
LassoCV Lasso linear model with iterative fitting along a regularization path. sklearn\linear_model_coordinate_descent.py
LassoCV Lasso linear model with iterative fitting along a regularization path. sklearn\linear_model_coordinate_descent.py
LassoCV Lasso linear model with iterative fitting along a regularization path. LassoLarsCV: Cross-validated Lasso, using the LARS algorithm. sklearn\linear_model_least_angle.py
LassoCV Lasso linear model with iterative fitting along a regularization path. sklearn\linear_model_least_angle.py
LassoCV Lasso linear model with iterative fitting along a regularization path. sklearn\linear_model_least_angle.py
LassoCV Lasso linear model with iterative fitting along a regularization path. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_omp.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. LassoLarsCV: Cross-validated Lasso, using the LARS algorithm. sklearn\linear_model_least_angle.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_coordinate_descent.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_omp.py
LassoLars Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn\linear_model_coordinate_descent.py
LassoLars Lasso Path along the regularization parameter usingLARS algorithm. sklearn\linear_model_coordinate_descent.py
LassoLarsCV Lasso least angle parameter algorithm by cross-validation. sklearn\linear_model_coordinate_descent.py
LassoLarsCV Cross-validated Lasso model fit with Least Angle Regression. sklearn\linear_model_omp.py
LassoLarsCV Cross-validated Lasso, using the LARS algorithm. sklearn\linear_model_least_angle.py
LassoLarsCV Cross-validated Lasso, using the LARS algorithm. sklearn\linear_model_least_angle.py
LassoLarsCV Cross-validated Lasso using the LARS algorithm. sklearn\linear_model_coordinate_descent.py
MiniBatchDictionaryLearning A faster, less accurate, version of the dictionary learning algorithm. sklearn\decomposition_dict_learning.py
MiniBatchDictionaryLearning A faster, less accurate, version of the dictionary learning algorithm. sklearn\decomposition_dict_learning.py
MiniBatchDictionaryLearning A faster, less accurate version of the dictionary learning algorithm. sklearn\decomposition_dict_learning.py
MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis. sklearn\decomposition_nmf.py
MiniBatchSparsePCA Mini batch variant of SparsePCA that is faster but less accurate. sklearn\decomposition_sparse_pca.py
MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis. sklearn\decomposition_fastica.py
MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
MultiTaskElasticNet Multi-task ElasticNet model trained with L1/L2 mixed-norm \ as regularizer. sklearn\linear_model_coordinate_descent.py
MultiTaskElasticNet Multi-task L1/L2 ElasticNet with built-in cross-validation. sklearn\linear_model_coordinate_descent.py
MultiTaskElasticNet Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer. sklearn\linear_model_coordinate_descent.py
MultiTaskElasticNetCV Multi-task L1/L2 ElasticNet with built-in cross-validation. sklearn\linear_model_coordinate_descent.py
MultiTaskElasticNetCV Multi-task L1/L2 ElasticNet with built-in cross-validation. sklearn\linear_model_coordinate_descent.py
NMF Non-negative matrix factorization. sklearn\decomposition_nmf.py
NMF Non-Negative Matrix Factorization. sklearn\decomposition_truncated_svd.py
NMF Non-Negative Matrix Factorization. sklearn\decomposition_kernel_pca.py
NearestNeighbors Unsupervised learner for implementing neighbor searches. sklearn\neighbors_regression.py
NearestNeighbors Regression based on nearest neighbors. sklearn\neighbors_regression.py
OrdinalEncoder Performs an ordinal (integer) encoding of the categorical features. sklearn\preprocessing_encoders.py
OrdinalEncoder Encode categorical features using an ordinal encoding scheme. sklearn\preprocessing_label.py
OrthogonalMatchingPursuit Orthogonal Matching Pursuit model (OMP). sklearn\linear_model_omp.py
OrthogonalMatchingPursuit Orthogonal Matching Pursuit model (OMP). sklearn\linear_model_omp.py
OrthogonalMatchingPursuit Orthogonal Matching Pursuit model. sklearn\linear_model_omp.py
PCA Principal component analysis. sklearn\decomposition_sparse_pca.py
PCA Principal component analysis (PCA). sklearn\decomposition_fastica.py
PCA Principal component analysis. sklearn\decomposition_nmf.py
PCA Principal Component Analysis implementation. sklearn\decomposition_sparse_pca.py
PCA Principal Component Analysis. sklearn\decomposition_kernel_pca.py
PCA Principal component analysis (PCA). sklearn\decomposition_incremental_pca.py
PrecisionRecallDisplay.from_estimator Plot Precision Recall Curve given a binary classifier. sklearn\metrics_ranking.py
PrecisionRecallDisplay.from_estimator Plot precision-recall curve given an estimator and some data. sklearn\metrics_classification.py
PrecisionRecallDisplay.from_estimator Plot precision-recall curve given an estimator and some data. sklearn\metrics_classification.py
PrecisionRecallDisplay.from_estimator Plot Precision Recall Curve given a binary classifier. sklearn\metrics_plot\precision_recall_curve.py
PrecisionRecallDisplay.from_predictions Plot Precision Recall Curve using predictions from a binary classifier. sklearn\metrics_ranking.py
PrecisionRecallDisplay.from_predictions Plot precision-recall curve given binary class predictions. sklearn\metrics_classification.py
RFECV Recursive feature elimination with built-in cross-validated selection of the best number of features. sklearn\feature_selection_rfe.py
RFECV Recursive feature elimination based on importance weights, with automatic selection of the number of features. sklearn\feature_selection_sequential.py
RFECV Recursive feature elimination with built-in cross-validated selection of the best number of features. sklearn\feature_selection_from_model.py
RadiusNeighborsRegressor Regression based on neighbors within a fixed radius. sklearn\neighbors_regression.py
RadiusNeighborsRegressor Regression based on neighbors within a fixed radius. sklearn\neighbors_classification.py
RadiusNeighborsRegressor Regression based on neighbors within a fixed radius. sklearn\neighbors_unsupervised.py
RandomForestClassifier A random forest classifier with optimal splits. sklearn\ensemble_forest.py
RandomForestClassifier A random forest classifier with optimal splits. sklearn\ensemble_forest.py
RandomForestClassifier A random forest classifier. sklearn\ensemble_forest.py
RandomForestClassifier A meta-estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. sklearn\ensemble_gb.py
RandomForestClassifier A meta-estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. sklearn\ensemble_hist_gradient_boosting\gradient_boosting.py
RandomForestRegressor A meta-estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the statistical performance and control over-fitting. sklearn\ensemble_hist_gradient_boosting\gradient_boosting.py
RandomForestRegressor A random forest regressor. sklearn.tree.ExtraTreeClassifier: An extremely randomized tree classifier. sklearn\ensemble_forest.py
RegressorChain Equivalent for regression. sklearn\multioutput.py
RegressorChain A multi-label model that arranges regressions into a chain. sklearn\multioutput.py
Ridge Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization. sklearn\linear_model_base.py
Ridge Ridge regression. sklearn\linear_model_ridge.py
Ridge Ridge regression. sklearn\linear_model_ridge.py
Ridge Ridge regression. sklearn\linear_model_ridge.py
Ridge Linear least squares with l2 regularization. sklearn\linear_model_stochastic_gradient.py
RidgeClassifier Ridge classifier. sklearn\linear_model_ridge.py
RidgeClassifier Ridge classifier. sklearn\linear_model_ridge.py
RidgeClassifier Classifier based on ridge regression on {-1, 1} labels. sklearn\linear_model_ridge.py
RocCurveDisplay.from_estimator Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data. sklearn\metrics_ranking.py
RocCurveDisplay.from_estimator ROC Curve visualization given an estimator and some data. sklearn\metrics_plot\roc_curve.py
RocCurveDisplay.from_estimator Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data. sklearn\metrics_ranking.py
RocCurveDisplay.from_estimator Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data. sklearn\metrics_plot\roc_curve.py
RocCurveDisplay.from_predictions Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values. sklearn\metrics_plot\roc_curve.py
RocCurveDisplay.from_predictions Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values. det_curve: Compute error rates for different probability thresholds. sklearn\metrics_ranking.py
RocCurveDisplay.from_predictions ROC Curve visualization given the probabilities of scores of a classifier. sklearn\metrics_plot\roc_curve.py
SGDClassifier Incrementally trained logistic regression. sklearn\linear_model_passive_aggressive.py
SGDClassifier Incrementally trained logistic regression (when given the parameter loss="log"). sklearn\linear_model_logistic.py
SelectPercentile Select features based on percentile of the highest scores. sklearn\feature_selection_univariate_selection.py
SelectPercentile Select features based on percentile of the highest scores. sklearn\feature_selection_univariate_selection.py
SelectPercentile Select features based on percentile of the highest scores. sklearn\feature_selection_univariate_selection.py
SelectPercentile Select features based on percentile of the highest scores. sklearn\feature_selection_univariate_selection.py
SelectPercentile Select features according to a percentile of the highest scores. sklearn\feature_selection_variance_threshold.py
SimpleImputer Univariate imputation of missing values. sklearn\impute_base.py
SimpleImputer Univariate imputer for completing missing values with simple strategies. sklearn\impute_iterative.py
SimpleImputer Univariate imputer for completing missing values with simple strategies. sklearn\impute_knn.py
SkewedChi2Sampler Approximate feature map for "skewed chi-squared" kernel. sklearn\kernel_approximation.py
SkewedChi2Sampler Approximate feature map for "skewed chi-squared" kernel. sklearn\kernel_approximation.py
SkewedChi2Sampler Approximate feature map for "skewed chi-squared" kernel. sklearn\kernel_approximation.py
SkewedChi2Sampler Approximate feature map for "skewed chi-squared" kernel. sklearn\kernel_approximation.py
SparsePCA Sparse Principal Components Analysis. sklearn\decomposition_sparse_pca.py
SparsePCA Sparse Principal Component Analysis. sklearn\decomposition_pca.py
SparsePCA Sparse Principal Components Analysis. sklearn\decomposition_nmf.py
SparsePCA Sparse Principal Component Analysis. sklearn\decomposition_kernel_pca.py
SparsePCA Sparse Principal Components Analysis (SparsePCA). sklearn\decomposition_incremental_pca.py
SparsePCA Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
SparsePCA Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
SparsePCA Sparse Principal Components Analysis. sklearn\decomposition_dict_learning.py
StandardScaler Perform standardization that is faster, but less robust to outliers. sklearn\preprocessing_data.py
StandardScaler Standardize features by removing the mean and scaling to unit variance. sklearn\preprocessing_function_transformer.py
accuracy_score Compute the accuracy score. By default, the function will return the fraction of correct predictions divided by the total number of predictions. sklearn\metrics_classification.py
accuracy_score Compute the accuracy score. By default, the function will return the fraction of correct predictions divided by the total number of predictions. sklearn\metrics_classification.py
accuracy_score Function for calculating the accuracy score. sklearn\metrics_classification.py
average_precision_score Compute average precision from prediction scores. det_curve: Compute error rates for different probability thresholds. sklearn\metrics_ranking.py
average_precision_score Compute average precision from prediction scores. sklearn\metrics_ranking.py
average_precision_score Compute average precision (AP) from prediction scores. sklearn\metrics_classification.py
average_precision_score Area under the precision-recall curve. sklearn\metrics_ranking.py
balanced_accuracy_score Compute the balanced accuracy to deal with imbalanced datasets. sklearn\metrics_classification.py
balanced_accuracy_score Compute balanced accuracy to deal with imbalanced datasets. sklearn\metrics_classification.py
chi2 Chi-squared stats of non-negative features for classification tasks. mutual_info_classif: Mutual information for a discrete target. sklearn\feature_selection_univariate_selection.py
chi2 Chi-squared stats of non-negative features for classification tasks. sklearn\feature_selection_univariate_selection.py
chi2 Chi-squared stats of non-negative features for classification tasks. sklearn\feature_selection_univariate_selection.py
chi2 Chi-squared stats of non-negative features for classification tasks. sklearn\feature_selection_univariate_selection.py
chi2 Chi-squared stats of non-negative features for classification tasks. sklearn\feature_selection_univariate_selection.py
chi2 Chi-squared stats of non-negative features for classification tasks. sklearn\feature_selection_univariate_selection.py
lars_path Regularization path using LARS. sklearn\linear_model_coordinate_descent.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_coordinate_descent.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_least_angle.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_omp.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_omp.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_omp.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_omp.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_coordinate_descent.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_least_angle.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_least_angle.py
lars_path Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn\linear_model_least_angle.py
lars_path_gram Compute LARS path. sklearn\linear_model_least_angle.py
lars_path_gram Compute LARS path in the sufficient stats mode. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_least_angle.py
lasso_path Compute Lasso path with coordinate descent. sklearn\linear_model_coordinate_descent.py
lasso_path Regularization path using Lasso. sklearn\linear_model_coordinate_descent.py
mutual_info_regression Mutual information for a continuous target. sklearn\feature_selection_univariate_selection.py
mutual_info_regression Mutual information for a continuous target. sklearn\feature_selection_univariate_selection.py
mutual_info_regression Mutual information for a contnuous target. sklearn\feature_selection_univariate_selection.py
mutual_info_regression Mutual information for a continuous target. sklearn\feature_selection_univariate_selection.py
orthogonal_mp_gram Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. sklearn\linear_model_omp.py
orthogonal_mp_gram Solve OMP problems using Gram matrix and the product X.T * y. sklearn\linear_model_omp.py
orthogonal_mp_gram Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. sklearn\linear_model_omp.py
precision_recall_fscore_support Compute precision, recall, F-measure and support for each class. sklearn\metrics_classification.py
precision_recall_fscore_support Compute the precision, recall, F-score, and support. sklearn\metrics_classification.py
precision_recall_fscore_support Compute the precision, recall, F-score, and support. sklearn\metrics_classification.py
precision_recall_fscore_support Compute precision, recall, F-measure and support for each class. sklearn\metrics_classification.py
precision_score Compute the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. sklearn\metrics_classification.py
precision_score Compute the precision score. sklearn\metrics_classification.py
recall_score Compute the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. sklearn\metrics_classification.py
recall_score Compute the recall score. sklearn\metrics_classification.py
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