X_train, X_test, y_train, y_test = train_test_split(data[['Returns','Variance']],data.cluster) ##plt.scatter(X_train, y_train, marker='+',color='green') ##plt.scatter(X_test, y_test, marker='*',color='purple')#ValueError: x and y must be the same size model = LogisticRegression() model.fit(X_train,y_train) y_predicted = model.predict(X_test) model.score(X_test,y_test)
时间: 2023-12-24 20:28:25 浏览: 28
这段代码是一个完整的机器学习流程,首先使用train_test_split函数将数据集分为训练集和测试集,其中训练集包含了Returns和Variance两个特征,测试集包含了对应的聚类标签cluster。然后使用LogisticRegression模型进行训练,使用训练好的模型对测试集进行预测,并计算模型在测试集上的准确率得分。
在代码中,由于plt.scatter(X_train, y_train, marker='+',color='green')和plt.scatter(X_test, y_test, marker='*',color='purple')这两行代码被注释掉了,因此不会绘制出训练集和测试集的散点图。而报错的原因是plt.scatter(X_train, y_train, marker='+',color='green')和plt.scatter(X_test, y_test, marker='*',color='purple')这两行代码中的X_train, y_train, X_test和y_test的维度不一致,不能绘制散点图。
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X_train, X_test, y_train, y_test = train_test_split(data[['Returns','Variance']],data.cluster)
这段代码使用了sklearn库中的train_test_split函数,对一个包含特征和标签的数据集data进行了随机划分,将数据划分为训练集和测试集,分别保存在X_train, X_test, y_train, y_test四个变量中。其中,特征包括'Returns'和'Variance'两列,标签为'cluster'列。这样做的目的是为了在模型训练时使用训练集进行训练,在测试时使用测试集进行评估,从而评估模型的泛化能力。
x_train = scaler.fit_transform(x_train)
This line of code is using the `fit_transform` method of the `scaler` object to scale the `x_train` data.
The `fit_transform` method is a convenient way to first fit the scaler to the data (i.e. calculate the mean and standard deviation of the data) and then transform the data using the calculated parameters.
The `scaler` object is typically an instance of a class from the `sklearn.preprocessing` module, such as `StandardScaler`, `MinMaxScaler`, or `RobustScaler`. These scalers are commonly used to preprocess data for machine learning algorithms by scaling features to have zero mean and unit variance or scaling features to a specific range.
In this case, `scaler.fit_transform(x_train)` is scaling the `x_train` data using the `fit_transform` method of the `scaler` object. The scaled data is then assigned back to `x_train`.