Question

在sklearn的Boston的帮助下，我尝试对RandomForestRegressor数据集使用随机森林算法来预测房价medv。

下面是我的训练/测试数据拆分：

'''Train Test Split of Data'''
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1)

Dimensions of Train/Test split X.shape: (489, 11) X_train.shape: (366, 11) X_test.shape: (123, 11)

下面是我的调整随机森林模型：

#1. import the class/model
from sklearn.ensemble import RandomForestRegressor

#2. Instantiate the estimator
RFReg = RandomForestRegressor(max_features = 'auto', random_state = 1, n_jobs = -1, max_depth = 14, min_samples_split = 2, n_estimators = 550) 

#3. Fit the model with data aka model training
RFReg.fit(X_train, y_train)

#4. Predict the response for a new observation
y_pred = RFReg.predict(X_test)


y_pred_train = RFReg.predict(X_train)

只需评估我使用以下代码尝试sklearn的learning curve的模型的效果如何

train_sizes = [1, 25, 50, 100, 200, 390] # 390 is 80% of shape(X)

from sklearn.model_selection import learning_curve
def learning_curves(estimator, X, y, train_sizes, cv):
    train_sizes, train_scores, validation_scores = learning_curve(
                                                 estimator, X, y, train_sizes = train_sizes,
                                                 cv = cv, scoring = 'neg_mean_squared_error')
    #print('Training scores:\n\n', train_scores)
    #print('\n', '-' * 70) # separator to make the output easy to read
    #print('\nValidation scores:\n\n', validation_scores)
    train_scores_mean = -train_scores.mean(axis = 1)
    print(train_scores_mean)
    validation_scores_mean = -validation_scores.mean(axis = 1)
    print(validation_scores_mean)

    plt.plot(train_sizes, train_scores_mean, label = 'Training error')
    plt.plot(train_sizes, validation_scores_mean, label = 'Validation error')

    plt.ylabel('MSE', fontsize = 14)
    plt.xlabel('Training set size', fontsize = 14)
    title = 'Learning curves for a ' + str(estimator).split('(')[0] + ' model'
    plt.title(title, fontsize = 18, y = 1.03)
    plt.legend()
    plt.ylim(0,40)

如果您注意到我已经通过X, y而不是X_train, y_train到learning_curve。

我对learning_curve

有以下疑问

我只是不明白是传递整个数据集，而不是仅传递train subset是正确的还是不正确的
测试数据集的大小是否根据列表train_sizes中所述的火车数据集的大小而变化，或者始终是固定的（根据火车/测试划分，在我的情况下为25％例如123个样本）
- 当train dataset size = 1时，测试数据大小将为488还是123（X_test的大小）
- 当train dataset size = 25时，测试数据大小将为464还是123（X_test的大小）
- 当train dataset size = 50时，测试数据大小将为439还是123（X_test的大小）

我对learning_curve函数中的训练/测试的大小感到困惑

Answer 1

您绝对只想使用训练测验，因此以这种方式调用该函数，原因是您希望了解实际使用的数据是如何进行学习的：

public Task<TurnBasedMatch> acceptInvitation (String invitationId)

学习曲线Sklearn

1 个答案: