随机森林优化demo

系统管理员 2021-09-10 04:18 407阅读 0赞

参考：[https://blog.csdn.net/y0367/article/details/51501780][https_blog.csdn.net_y0367_article_details_51501780]

import numpy as np
    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier
    train = pd.read_csv("train.csv", dtype={"Age": np.float64},)
    print(train.head(10))
    
    print(len(train))
    
    def harmonize_data(titanic):
        # 填充空数据 和 把string数据转成integer表示
        # 对于年龄字段发生缺失，我们用所有年龄的均值替代
        titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
        # 性别男： 用0替代
        titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
        # 性别女： 用1替代
        titanic.loc[titanic["Sex"] == "female", "Sex"] = 1
    
        titanic["Embarked"] = titanic["Embarked"].fillna("S")
    
        titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
        titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
        titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2
    
        titanic["Fare"] = titanic["Fare"].fillna(titanic["Fare"].median())
    
        return titanic
    
    train_data = harmonize_data(train)
    
    # 列出对生存结果有影响的字段
    predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
    # 存放不同参数取值，以及对应的精度，每一个元素都是一个三元组(a, b, c)
    results = []
    # 最小叶子结点的参数取值
    sample_leaf_options = list(range(1, 500, 3))
    # 决策树个数参数取值
    n_estimators_options = list(range(1, 1000, 5))
    groud_truth = train_data['Survived'][601:]
    
    for leaf_size in sample_leaf_options:
        for n_estimators_size in n_estimators_options:
            alg = RandomForestClassifier(min_samples_leaf=leaf_size, n_estimators=n_estimators_size, random_state=50)
            alg.fit(train_data[predictors][:600], train_data['Survived'][:600])
            predict = alg.predict(train_data[predictors][601:])
            # 用一个三元组，分别记录当前的 min_samples_leaf，n_estimators， 和在测试数据集上的精度
            results.append((leaf_size, n_estimators_size, (groud_truth == predict).mean()))
            # 真实结果和预测结果进行比较，计算准确率
            print((groud_truth == predict).mean())
    
    # 打印精度最大的那一个三元组
    print(max(results, key=lambda x: x[2]))

[https_blog.csdn.net_y0367_article_details_51501780]: https://link.jianshu.com?t=https%3A%2F%2Fblog.csdn.net%2Fy0367%2Farticle%2Fdetails%2F51501780