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Machine LearningPython # Import libraries and modules import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import make_pipeline from sklearn.model_selection import GridSearchCV from sklearn.metrics import mean_squared_error, r2_score from sklearn.externals import joblib # Load red wine data. dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv' data = pd.read_csv(dataset_url, sep=';') # Split data into training and test sets y = data.quality X = data.drop('quality', axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123, stratify=y) # Declare data preprocessing steps pipeline = make_pipeline(preprocessing.StandardScaler(), RandomForestRegressor(n_estimators=100)) # Declare hyperparameters to tune hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'], 'randomforestregressor__max_depth': [None, 5, 3, 1]} # Tune model using cross-validation pipeline clf = GridSearchCV(pipeline, hyperparameters, cv=10) clf.fit(X_train, y_train) # Refit on the entire training set # No additional code needed if clf.refit == True (default is True) # Evaluate model pipeline on test data pred = clf.predict(X_test) print r2_score(y_test, pred) print mean_squared_error(y_test, pred) # Save model for future use joblib.dump(clf, 'rf_regressor.pkl') # To load: clf2 = joblib.load('rf_regressor.pkl') |
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