X= [X_full,X_missing_mean,X_missing,X_missing_reg] mse =[] for x in X: estimator = RandomForestRegressor(random_state=0, n_estimators=100) scores = cross_val_score(estimator,x,y_full,scoring='neg_mean_squared_error',cv=5).mean() #看均方误差 mse.append(scores*-1) #越小越好 mse

The code you provided seems to be calculating the mean squared error (MSE) for different versions of the input data `X` using a RandomForestRegressor model. It then appends the negative MSE to a list `mse` for each version of the data.

To clarify, I assume that `X_full` is the complete dataset, `X_missing_mean` is the dataset with missing values filled using mean imputation, `X_missing` is the dataset with missing values as-is, and `X_missing_reg` is the dataset with missing values filled using regression imputation.

Here is the modified code with proper indentation:

X = [X_full, X_missing_mean, X_missing, X_missing_reg]
mse = []

for x in X:
    estimator = RandomForestRegressor(random_state=0, n_estimators=100)
    scores = cross_val_score(estimator, x, y_full, scoring='neg_mean_squared_error', cv=5).mean()
    mse.append(scores * -1)

mse

The code calculates the negative MSE for each version of the data and stores it in the `mse` list. The lower the MSE, the better the model's performance.