DataFrame contains the features total bill amount and size. And the df_target DataFrame contains the tip amount in a column.

df_features = df.filter(items=["total_bill", "size"])
df_target = df.filter(items=["tip"])

Now, we are splitting the dataset into train and test set. The size of the test set is 20% of the dataset. And we are shuffling the dataset before splitting the dataset. The random_state=1 parameter is used to control the random number generator that is used for shuffling.

X_train, X_test, y_train, y_test = train_test_split(df_features, df_target["tip"], test_size=0.2, shuffle=True, random_state=1)

Now, we are initializing the random forest regressor. The randomness of the bootstrapping of the samples or the sampling of the features is controlled by the random_state=1 parameter.

The random forest regressor uses the fit() method to learn from the dataset. And the predict() method is used to predict the target variable for the test set.

regressor = RandomForestRegressor(random_state=1)
regressor.fit(X_train, y_train)
y_test_pred = regressor.predict(X_test)

Now, we can compare the y_test_pred and y_test to measure the performance of the model. Here, we are calculating the R-squared score and the Root Mean Square Error (RMSE).

r2 = r2_score(y_test, y_test_pred)
rmse = mean_squared_error(y_test, y_test_pred, squared=False)

The output of the above program will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   total_bill  244 non-null    float64 
 1   tip         244 non-null    float64 
 2   sex         244 non-null    category
 3   smoker      244 non-null    category
 4   day         244 non-null    category
 5   time        244 non-null    category
 6   size        244 non-null    int64   
dtypes: category(4), float64(2), int64(1)
memory usage: 7.4 KB
None
   total_bill  size
0       16.99     2
1       10.34     3
2       21.01     3
3       23.68     2
4       24.59     4
    tip
0  1.01
1  1.66
2  3.50
3  3.31
4  3.61
R2 Score:  0.4971554940373689
RMSE:  1.150371809730061