模型选择与调优

1、交叉验证（Cross Validation）

分析：

2、超参数搜索 - 网格搜索（Grid Search）

3、模型选择与调优API

ex_1

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV

def knn_iris():
    '''
    用KNN算法对鸢尾花进行分类
    :return:
    '''
    # 1）获取数据
    iris = load_iris()
    # 2）划分数据集
    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=6)

    # 3）特征工程：标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4）KNN短发预估器
    estimator = KNeighborsClassifier(n_neighbors=3)
    estimator.fit(x_train, y_train)
    # 有了模型
    # 5）模型评估
    # 方法1：直接对比真实值和预测值
    y_predict = estimator.predict(x_test)
    print("y_predict: \n", y_predict)
    print("直接对比真实值和预测值: \n", y_test == y_predict)

    # 方法2：计算准确率
    score = estimator.score(x_test, y_test)
    print("准确率：\n", score)
    return None


def knn_iris_gscv():
    """
    用KNN算法对鸢尾花进行分类，添加网格搜索和交叉验证
    :return:
    """
    # 1）获取数据
    iris = load_iris()

    # 2）划分数据集
    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)

    # 3）特征工程：标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)

    # 4）KNN算法预估器
    estimator = KNeighborsClassifier()

    # 加入网格搜索与交叉验证
    # 参数准备
    param_dict = {"n_neighbors": [1, 3, 5, 7, 9, 11]}
    estimator = GridSearchCV(estimator, param_grid=param_dict, cv=10)
    estimator.fit(x_train, y_train)

    # 5）模型评估
    # 方法1：直接比对真实值和预测值
    y_predict = estimator.predict(x_test)
    print("y_predict:\n", y_predict)
    print("直接比对真实值和预测值:\n", y_test == y_predict)

    # 方法2：计算准确率
    score = estimator.score(x_test, y_test)
    print("准确率为：\n", score)

    # 最佳参数：best_params_
    print("最佳参数：\n", estimator.best_params_)
    # 最佳结果：best_score_
    print("最佳结果：\n", estimator.best_score_)
    # 最佳估计器：best_estimator_
    print("最佳估计器:\n", estimator.best_estimator_)
    # 交叉验证结果：cv_results_
    print("交叉验证结果:\n", estimator.cv_results_)

    return None