Why does min-max scaler result in lower accuracy with regression tree?

Question

I have a dataset that contains 7 features. Values are not too large. I trained scikit-learn's RandomForestRegressor for predicting the target variable. The $R^2$ score turns out to be something aroung 0.2, but when I first scale x_train and x_test using scitkit-learn's MinMaxScaler, the $R^2$ score drops to 0.11. I heard decision trees are not affected by scaling so it is not necessary to scale the data. My question is why would the score drop so much if trees are not affected by scaling?

This is without scaling

regressor = RandomForestRegressor()
predictions = fit_predict(X_train, y_train, X_test)
evaluate(y_test, predictions)

This is with scaling

scaler = preprocessing.MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.fit_transform(X_test)
regressor = RandomForestRegressor()
predictions = fit_predict(X_train_scaled, y_train, X_test_scaled)
evaluate(y_test, predictions)

Answer 1

Your error is that you train your model to work on the training data after a scaling operation that you defined (fit) on the training. But then to evaluate using your test data you refit the scaler on the test data, meaning you are going to apply a different scaling to the test set, than you did on the training data to train the model.

You need to not refit the scaler on the test set and just apply the transformation:

X_test_scaled = scaler.transform(X_test)