How to use the same scale with new data? - scikit learn - scikit learn

Question

How do I use the same scale used in preprocessing with new data.

Actual code:

x = df.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df_scaled = pd.DataFrame(x_scaled)

clf = tree.DecisionTreeClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)

If I understand it correctly I should have included a scaler variable with the StandardScaler.

https://stackoverflow.com/questions/38780302/predicting-new-data-using-sklearn-after-standardizing-the-training-data

Something like:

clf = tree.DecisionTreeClassifier()
clf.fit(X_train, y_train)
scaler = preprocessing.StandardScaler().fit(X_train)
pred = clf.predict(X_test)

What scaler parameters should I use for future data processing?

Thanks!

Answer 1

IMO, you don't need to use scaling if your classifiers are based on decision trees. Also, in your final piece of code, the variable scaler is never used, so I am not sure at all why it is defined. Nevertheless, if you insist on using a scaler, you should Pipeline it so it automatically applies to the test data the same scaling it has learned by fitting to training data:

pipeline = Pipeline([('scaler', StandardScaler()), ('classifier', DecisionTreeClassifier())])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

Note that you'd obviously need to import the necessary modules (I was too lazy to write them here!).