如何用 Python 构建机器学习模型？

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。

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)

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)

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)

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)

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)

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)

# 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)

# 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)

# 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)

# 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

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