Turning a Pandas Dataframe to an array and evaluate Multiple Linear Regression Model

Question

I am trying to evaluate a multiple linear regression model. I have a data set like this :

Answer 1

You can turn the dataframe into a matrix using the method as_matrix directly on the dataframe object. You might need to specify the columns which you are interested in X=df[['x1','x2','X3']].as_matrix() where the different x's are the column names.

For the y variables you can use y = df['ground_truth'].values to get an array.

Here is an example with some randomly generated data:

import numpy as np
#create a 5X5 dataframe
df = pd.DataFrame(np.random.random_integers(0, 100, (5, 5)), columns = ['X1','X2','X3','X4','y'])

calling as_matrix() on df returns a numpy.ndarray object

X = df[['X1','X2','X3','X4']].as_matrix()

Calling values returns a numpy.ndarray from a pandas series

y =df['y'].values

Notice: You might get a warning saying:FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.

To fix it use values instead of as_matrix as shown below

X = df[['X1','X2','X3','X4']].values

Answer 2

y = broken_df.ground_truth.values
X = broken_df.drop('ground_truth', axis=1).values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
linreg = LinearRegression()
linreg.fit(X_train, y_train)
y_pred = linreg.predict(X_test)
print(linreg.score(X_test, y_test)
print(classification_report(y_test, y_pred))