# Why after adding categorical data the Linear Regression fails?

Based on a training set we applied a simple Linear Regression on some attributes that all were numeric.

Now we have more attributes in terms of categories and of course we applied one-hot-encoding to transform the categories to binary attributes

Take for example this simple python code:

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size=0.2)

model = LinearRegression(normalize=True).fit(X_train, y_train)

printErrorMetrics(trueTargets=y_test, predictions=model.predict(X_test))


When the table X has only the original numeric attributes the scores from the printErrorMetrics function (RMSE, etc.) are all good enough

We were expecting better results after adding the one-hot-encoded categories but the results are so worse that the method does not seem to work.

Are we missing anything?

Do we need to preprocess the data after adding the one-hot-encoded columns/attributes?

model = LinearRegression(fit_intercept=False, normalize=True).fit(X_train, y_train)