将随机森林预测作为列添加到测试文件中 [英] add random forest predictions as column into test file

问题描述

我正在使用python pandas(在Jupyter笔记本中)工作，在其中为Titanic数据集创建了一个随机森林模型. https://www.kaggle.com/c/titanic/data

I am working in python pandas (in a Jupyter notebook), where I created a Random Forest model for the Titanic data set. https://www.kaggle.com/c/titanic/data

我读了测试并训练了数据，然后清理它并添加了新列(两个列都相同).

I read in the test and train data, then I clean it and I add new columns (the same columns to both).

在拟合和重新拟合模型并尝试增强效果之后；我决定使用一种模型:

After fitting and re-fitting the model and trying boosts etc; I decide on one model:

 X2 = train_data[['Pclass','Sex','Age','richness']] 
 rfc_model_3 = RandomForestClassifier(n_estimators=200)
 %time cross_val_score(rfc_model_3, X2, Y_target).mean()
 rfc_model_3.fit(X2, Y_target)

然后我预测，如果有人幸免于难

Then I predict, if somebody survived or not

 X_test = test_data[['Pclass','Sex','Age','richness']]
 predictions = rfc_model_3.predict(X_test)
 preds = pd.DataFrame(predictions, columns=['Survived'])

我是否可以将预测作为column添加到测试文件中?

Is there a way for me to add the predictions as a column into the test file?

推荐答案

自

rfc_model_3 = RandomForestClassifier(n_estimators=200)
rfc_model_3.predict(X_test)

返回y : array of shape = [n_samples](请参阅文档)，您应该能够将模型输出直接添加到X_test，而无需创建中间的DataFrame:

returns y : array of shape = [n_samples] (see docs), you should be able to add the model output directly to X_test without creating an intermediate DataFrame:

X_test['survived'] = rfc_model_3.predict(X_test)

如果您仍然想要中间结果，则@EdChum在评论中的建议会很好.

If you want the intermediate result anyway, @EdChum's suggestion in the comments would work fine.

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