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Author: Algorithmica

2019-october/6.overfit and underfit/overfit-underfit-objective based learning.py

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+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+from sklearn import model_selection, linear_model, datasets
+import numpy as np
+
+#generate data with noise(irrelevant) features
+X, y = datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=4, n_repeated=0, n_classes=2)
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
+
+np.corrcoef(X_train, rowvar=False)
+
+#overfit
+perceptron_estimator = linear_model.Perceptron(max_iter=1000)
+perceptron_grid  = {'alpha':[0] }
+final_estimator = cutils.grid_search_best_model(perceptron_estimator, perceptron_grid, X_train, y_train)
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+
+#overfit control
+perceptron_estimator = linear_model.Perceptron(max_iter=1000)
+perceptron_grid  = {
+        'penalty':['l1', 'l2'], 'alpha':[0, 0.00001, 0.0001, 0.0005, 0.001, 0.01, 0.1, 0.2, 0.5, 1, 3] }
+final_estimator = cutils.grid_search_best_model(perceptron_estimator, perceptron_grid, X_train, y_train)
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+
+#overfitting
+lr_estimator = linear_model.LogisticRegression()
+lr_grid  = {'C':[1e20] }
+final_estimator = cutils.grid_search_best_model(lr_estimator, lr_grid, X_train, y_train)
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+
+#overfit control
+lr_estimator = linear_model.LogisticRegression()
+lr_grid  = {'penalty':['l1', 'l2'], 'C':[0.001, 0.1, 0.5, 1, 10, 1e2] }
+final_estimator = cutils.grid_search_best_model(lr_estimator, lr_grid, X_train, y_train)
+print(final_estimator.intercept_)
+print(final_estimator.coef_)