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Importance of Feature Scaling — scikit-learn 1....
Feature scaling through standardization, also called Z-score normalization, is an important preprocessing step for many machine learning algorithms. It involves rescaling each feature such that it ...scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html -
SVM Margins Example — scikit-learn 1.7.2 docume...
The plots below illustrate the effect the parameter C has on the separation line. A large value of C basically tells our model that we do not have that much faith in our data’s distribution, and wi...scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html -
classification_report — scikit-learn 1.7.2 docu...
Gallery examples: Faces recognition example using eigenfaces and SVMs Recognizing hand-written digits Column Transformer with Heterogeneous Data Sources Pipeline ANOVA SVM Custom refit strategy of ...scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html -
log_loss — scikit-learn 1.7.2 documentation
Gallery examples: Probability Calibration curves Probability Calibration for 3-class classification Plot classification probability Gradient Boosting Out-of-Bag estimates Gradient Boosting regulari...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
det_curve — scikit-learn 1.7.2 documentation
Gallery examples: Detection error tradeoff (DET) curvescikit-learn.org/stable/modules/generated/sklearn.metrics.det_curve.html -
roc_curve — scikit-learn 1.7.2 documentation
Gallery examples: Species distribution modeling Visualizations with Display Objects Detection error tradeoff (DET) curve Multiclass Receiver Operating Characteristic (ROC)scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html -
matthews_corrcoef — scikit-learn 1.7.2 document...
Skip to main content Back to top Ctrl + K GitHub Choose version matthews_corrcoef # sklearn.metrics. matthews_corrcoe...scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html -
Polynomial and Spline interpolation — scikit-le...
This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i: PolynomialFeatur...scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html -
Generalized Linear Models — scikit-learn 1.7.2 ...
Examples concerning the sklearn.linear_model module. Comparing Linear Bayesian Regressors Curve Fitting with Bayesian Ridge Regression Decision Boundaries of Multinomial and One-vs-Rest Logistic Re...scikit-learn.org/stable/auto_examples/linear_model/index.html -
Recursive feature elimination — scikit-learn 1....
This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html