Logistic Regression, Random Forest, Neural Networks, Image Processing, Computer Vision.
n_components = min(X_train_std.shape[1], len(np.unique(y_train))) - 1 pipeline2 = Pipeline([('pca', LinearDiscriminantAnalysis(n_components= n_components)), \ ('clf', RandomForestRegressor(n_estimators = 20, random_state=42))]) pipeline2.fit(X_train_std, y_train) print('Test Accuracy: %.3f'