Python 学习曲线
在sklearn的帮助下,我在数据集上尝试随机森林算法来预测房价Python 学习曲线,python,machine-learning,scikit-learn,random-forest,Python,Machine Learning,Scikit Learn,Random Forest,在sklearn的帮助下,我在数据集上尝试随机森林算法来预测房价medv 以下是我的列车/测试数据分割: '''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) 列车/试验拆分的尺寸 形状:(489,11) X_列车形状:(366,
medv
以下是我的列车/测试数据分割:
'''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)
列车/试验拆分的尺寸
形状:(489,11)
X_列车形状:(366,11)
X_测试形状:(123,11)
下面是我的优化随机森林模型:
#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)
为了评估模型的性能,我用下面的代码尝试了sklearn's
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)
如果您注意到我已通过X,y
而不是X\u列,y\u列
至学习曲线
关于learning\u curve
列车子集
是否正确train_size
中提到的列车数据集的大小而变化,或者它始终是固定的(在我的情况下,根据123个样本的列车/测试分割,这将是25%)
- 当
测试数据大小是488还是123(X_测试的大小)列车数据集大小=1时
- 当
测试数据大小是464还是123(X_测试的大小)列车数据集大小=25时
- 当
测试数据大小是439还是123(X_测试的大小)列车数据集大小=50时
我对
learning\u curve
function中的训练/测试的大小感到有点困惑,你肯定只想使用你的训练测试,所以这样调用函数,原因是你想看看你实际使用的数据是如何学习的:
learning_curves(estimator=RFReg, X=X_train, y=y_size, train_sizes= train_sizes)