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

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["Outcome"], test_size=0.2, shuffle=True, random_state=1)

Now, we are initializing the classifier. The DecisionTreeClassifier() constructor takes the parameter random_state=1 to control the randomness of the estimator. Please note that the features are randomly permuted at each split.

The fit() method learns from the dataset. And the predict() method is used to predict the target label for the test set.

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

Now, we can compare y_test_pred with y_test and measure the performance of the model. Here, we will use the accuracy score (What is the accuracy score?).

accuracy = accuracy_score(y_test, y_test_pred)

The output of the above 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:  0.6948051948051948

As we can see, the accuracy score is good but not great. In our next article, we will use a random forest classifier to improve this accuracy score.