如何用 Python 构建机器学习模型？ 作者 | Anello译者 | Sambodhi策划 | 凌敏本文，我们将通过 Python 语言包，来构建一些机器学习模型。构建机器学习模型的模板该 Notebook 包含了用于创建主要机器学习算法所需的代码模板。在 scikit-learn 中，我们已经准备好了几个算法。只需调整参数，给它们输入数据，进行训练，生成模型，最后进行预测。 1. 线性回归对于线性回归，我们需要从 sklearn 库中导入 linear_model。我们准备好训练和测试数据，然后将预测模型实例化为一个名为线性回归 LinearRegression 算法的对象，它是 linear_model 包的一个类，从而创建预测模型。之后我们利用拟合函数对算法进行训练，并利用得分来评估模型。最后，我们将系数打印出来，用模型进行新的预测。 # Import modules from sklearn import linear_model # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted_variable x_test = test_dataset_precictor_variables # Create linear regression object linear = linear_model.LinearRegression() # Train the model with training data and check the score linear.fit(x_train, y_train) linear.score(x_train, y_train) # Collect coefficients print('Coefficient: \n', linear.coef_) print('Intercept: \n', linear.intercept_) # Make predictions predicted_values = linear.predict(x_test) 2. 逻辑回归在本例中，从线性回归到逻辑回归唯一改变的是我们要使用的算法。我们将 LinearRegression 改为 LogisticRegression。 # Import modules from sklearn.linear_model import LogisticRegression # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted_variable x_test = test_dataset_precictor_variables # Create logistic regression object model = LogisticRegression() # Train the model with training data and checking the score model.fit(x_train, y_train) model.score(x_train, y_train) # Collect coefficients print('Coefficient: \n', model.coef_) print('Intercept: \n', model.intercept_) # Make predictions predicted_vaues = model.predict(x_teste) 3. 决策树我们再次将算法更改为 DecisionTreeRegressor： # Import modules from sklearn import tree # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted_variable x_test = test_dataset_precictor_variables # Create Decision Tree Regressor Object model = tree.DecisionTreeRegressor() # Create Decision Tree Classifier Object model = tree.DecisionTreeClassifier() # Train the model with training data and checking the score model.fit(x_train, y_train) model.score(x_train, y_train) # Make predictions predicted_values = model.predict(x_test) 4. 朴素贝叶斯我们再次将算法更改为 DecisionTreeRegressor： # Import modules from sklearn.naive_bayes import GaussianNB # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted variable x_test = test_dataset_precictor_variables # Create GaussianNB object model = GaussianNB() # Train the model with training data model.fit(x_train, y_train) # Make predictions predicted_values = model.predict(x_test) 5. 支持向量机在本例中，我们使用 SVM 库的 SVC 类。如果是 SVR，它就是一个回归函数： # Import modules from sklearn import svm # Create training and test subsets x_train = train_dataset_predictor_variables y_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 score model.fit(x_train, y_train) model.score(x_train, y_train) # Make predictions predicted_values = model.predict(x_test) 6.K- 最近邻在 KneighborsClassifier 算法中，我们有一个超参数叫做 n_neighbors，就是我们对这个算法进行调整。 # Import modules from sklearn.neighbors import KNeighborsClassifier # Create training and test subsets x_train = train_dataset_predictor_variables y_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 data model.fit(x_train, y_train) # Make predictions predicted_values = model.predict(x_test) 7.K- 均值# Import modules from sklearn.cluster import KMeans # Create training and test subsets x_train = train_dataset_predictor_variables y_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 data model.fit(x_train) # Make predictions predicted_values = model.predict(x_test) 8. 随机森林# Import modules from sklearn.ensemble import RandomForestClassifier # Create training and test subsets x_train = train_dataset_predictor_variables y_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 predictions predicted_values = model.predict(x_test) 9. 降维# Import modules from sklearn import decomposition # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted variable x_test = test_dataset_precictor_variables # Creating PCA decomposition object pca = decomposition.PCA(n_components = k) # Creating Factor analysis decomposition object fa = decomposition.FactorAnalysis() # Reduc the size of the training set using PCA reduced_train = pca.fit_transform(train) # Reduce the size of the training set using PCA reduced_test = pca.transform(test) 10. 梯度提升和 AdaBoost# Import modules from sklearn.ensemble import GradientBoostingClassifier # Create training and test subsets x_train = train_dataset_predictor_variables y_train = train_dataset_predicted variable x_test = test_dataset_precictor_variables # Creating Gradient Boosting Classifier object model = 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 predictions predicted_values = model.predict(x_test) 我们的工作将是把这些算法中的每一个块转化为一个项目。首先，定义一个业务问题，对数据进行预处理，训练算法，调整超参数，获得可验证的结果，在这个过程中不断迭代，直到我们达到满意的精度，做出理想的预测。 原文链接： https://levelup.gitconnected.com/10-templates-for-building-machine-learning-models-with-notebook-282c4eb0987f 你也「在看」吗？?? 阅读原文