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plot_adaboost_regression.rst.txt
y: ========== Decision Tree Regression with AdaBoost ==========...y_1, color=colors[1], label="n_estimators=1", linewidth=2) plt.plot(X,...scikit-learn.org/stable/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt -
Target Encoder’s Internal Cross fitting — sciki...
transform_output = "pandas" ) ridge = Ridge ( alpha = 1e-6 , solver = "lsqr"...n_samples = 50_000 rng = np . random . RandomState ( 42 ) y = rng ....scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Scalable learning with polynomial kernel approx...
y = fetch_covtype ( return_X_y = True ) y [ y != 2 ] = 0 y...SVC ksvm = SVC ( C = 500.0 , kernel = "poly" , degree = 4 , coef0...scikit-learn.org/stable/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html -
SVM Tie Breaking Example — scikit-learn 1.5.0 d...
()): svm = SVC ( kernel = "linear" , C = 1 , break_ties = break_ties...SVC X , y = make_blobs ( random_state = 27 ) fig , sub = plt . subplots...scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html -
beta_divergence.png
encoding=ISO-8859-1, compression=none keyword=Software, value=Matplotlib...0.2540005 width=640, height=480, bitDepth=8, colorType=RGB, compr...scikit-learn.org/stable/_images/beta_divergence.png -
Restricted Boltzmann Machine features for digit...
mode = "constant" , weights = w ) . ravel () X = np . concatenate...X , y = datasets . load_digits ( return_X_y = True ) X = np ....scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html -
Single estimator versus bagging: bias-variance ...
* 10 - 5 X = np . sort ( X ) if n_repeat == 1 : y = f ( X ) +...X_train = [] y_train = [] for i in range ( n_repeat ): X , y = generate...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html -
GroupShuffleSplit — scikit-learn 1.5.0 document...
splits=2, random_state=42, test_size=None, train_size=0.7) >>>...n_splits = 5 , * , test_size = None , train_size = None , random_state...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
export_graphviz — scikit-learn 1.5.0 documentation
out_file = None , * , max_depth = None , feature_names = None ,..., class_names = None , label = 'all' , filled = False , leaves_parallel...scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html -
plot_release_highlights_1_4_0.ipynb
2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 *...time\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_...scikit-learn.org/stable/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0...
