Try to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:

from sklearn.metrics import mean_squared_error

print(mean_squared_error(Y_test, y_test_predicted).round(2))

print(mean_squared_error(Y_test, y_test_predicted2).round(2))


              123
            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))


python
9
1
2
3
Output: 
0.28
0.27

MAE:

from sklearn.metrics import mean_absolute_error

print(mean_absolute_error(Y_test, y_test_predicted).round(2))

print(mean_absolute_error(Y_test, y_test_predicted2).round(2))


              123
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))


python
9
1
2
3
Output: 
0.45
0.43

R-squared:

from sklearn.metrics import r2_score

print(r2_score(Y_test, y_test_predicted).round(2))

print(r2_score(Y_test, y_test_predicted2).round(2))


              123
            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))


python
9
1
2
3
Output: 
0.53
0.55

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Tehtävä

Swipe to start coding

Let’s evaluate the model from the previous task:

[Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
[Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
[Line #32] Print the variable MSE.
[Line #35] Import r2_score from scikit.metrics.
[Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
[Line #37] Print the variable r_squared.

Ratkaisu

# Import the libraries

import pandas as pd

from sklearn.datasets import load_wine

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

# Load the dataset

wine = load_wine()

# Configure pandas to show all features

pd.set_option('display.max_rows', None, 'display.max_columns', None)

# Define the DataFrame

data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Define the target

data['total_phenols'] = wine.target

# Prepare the data

X2 = data[['flavanoids', 'nonflavanoid_phenols']]

Y = data['total_phenols']

# Train and fit the model

X2_train, X2_test, Y_train, Y_test = train_test_split(X2, Y, test_size = 0.3, random_state = 1)

model2 = LinearRegression()

model2.fit(X2_train, Y_train)

y_test_predicted2 = model2.predict(X2_test)

# Calculate the MSE

from sklearn.metrics import mean_squared_error

MSE = mean_squared_error(Y_test, y_test_predicted2).round(2)

print(MSE)

# Calculate the R-squared