如何用 Python 构建机器学习模型？

# Import modulesfrom sklearn import linear_model
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test  = test_dataset_precictor_variables
# Create linear regression objectlinear = linear_model.LinearRegression()
# Train the model with training data and check the scorelinear.fit(x_train, y_train)linear.score(x_train, y_train)
# Collect coefficientsprint('Coefficient: \n', linear.coef_)print('Intercept: \n', linear.intercept_)
# Make predictionspredicted_values = linear.predict(x_test)

# Import modulesfrom sklearn.linear_model import LogisticRegression
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test  = test_dataset_precictor_variables
# Create logistic regression objectmodel = LogisticRegression()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Collect coefficientsprint('Coefficient: \n', model.coef_)print('Intercept: \n', model.intercept_)
# Make predictionspredicted_vaues = model.predict(x_teste)

# Import modulesfrom sklearn import tree
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test  = test_dataset_precictor_variables
# Create Decision Tree Regressor Objectmodel = tree.DecisionTreeRegressor()
# Create Decision Tree Classifier Objectmodel = tree.DecisionTreeClassifier()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn.naive_bayes import GaussianNB
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Create GaussianNB objectmodel = GaussianNB()
# Train the model with training data model.fit(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn import svm
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Create SVM Classifier object  model = svm.svc()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn.neighbors import KNeighborsClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Create KNeighbors Classifier Objects  KNeighborsClassifier(n_neighbors = 6) # default value = 5
# Train the model with training datamodel.fit(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn.cluster import KMeans
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Create KMeans objects k_means = KMeans(n_clusters = 3, random_state = 0)
# Train the model with training datamodel.fit(x_train)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn.ensemble import RandomForestClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Create Random Forest Classifier objects model = RandomForestClassifier()
# Train the model with training data model.fit(x_train, x_test)
# Make predictionspredicted_values = model.predict(x_test)

# Import modulesfrom sklearn import decomposition
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Creating PCA decomposition objectpca = decomposition.PCA(n_components = k)
# Creating Factor analysis decomposition objectfa = decomposition.FactorAnalysis()
# Reduc the size of the training set using PCAreduced_train = pca.fit_transform(train)
# Reduce the size of the training set using PCAreduced_test = pca.transform(test)

# Import modulesfrom sklearn.ensemble import GradientBoostingClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test  = test_dataset_precictor_variables
# Creating Gradient Boosting Classifier objectmodel = GradientBoostingClassifier(n_estimators = 100, learning_rate = 1.0, max_depth = 1, random_state = 0)
# Training the model with training data model.fit(x_train, x_test)
# Make predictionspredicted_values = model.predict(x_test)