df_features = df.drop(labels=["Outcome"], axis=1)
df_target = df.filter(items=["Outcome"])

Now, we are splitting the dataset into training and test set. The size of the test set is 20% of the dataset. We are shuffling the dataset before splitting. And the random_state 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["Outcome"], test_size=0.2, shuffle=True, random_state=1)

Now, we initialize the random forest classifier. The random_state parameter in the RandomForestClassifier() constructor is used to control the randomness of the bootstrapping of the samples and the sampling of the features.

The fit() method learns from the dataset and the predict() method is used to predict the target of the test set.

classifier = RandomForestClassifier(random_state=1)
classifier.fit(X_train, y_train)
y_test_pred = classifier.predict(X_test)

Now, we can compare y_test_pred and y_test to measure the performance of the random forest classifier. We will use accuracy score here (What is accuracy score?).

accuracy = accuracy_score(y_test, y_test_pred)

The output of the given program will be like the following:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
None
   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome
0            6      148             72  ...                     0.627   50        1
1            1       85             66  ...                     0.351   31        0
2            8      183             64  ...                     0.672   32        1
3            1       89             66  ...                     0.167   21        0
4            0      137             40  ...                     2.288   33        1

[5 rows x 9 columns]
   Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age
0            6      148             72  ...  33.6                     0.627   50
1            1       85             66  ...  26.6                     0.351   31
2            8      183             64  ...  23.3                     0.672   32
3            1       89             66  ...  28.1                     0.167   21
4            0      137             40  ...  43.1                     2.288   33

[5 rows x 8 columns]
   Outcome
0        1
1        0
2        1
3        0
4        1
Accuracy Score:  0.8051948051948052

As we can see, the accuracy score has improved. Previously, we solved the same problem using a classification tree. When we solved the same classification problem using a random forest classifier instead, the accuracy score improved.