Decision tree classifier

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import confusion_matrix, classification_report

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

from sklearn.tree import plot_tree

# Data load

from sklearn.datasets import load_iris

iris = load_iris()

# Data separation

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

y = iris['target']

# Split train : test data set = 7:3 and set random seed

X_train, X_test, y_train, y_test = train_test_split(X,

                                                    y,

                                                    test_size = 0.3,

                                                    random_state = 42,

stratify = y)

# Define a model structure

dtc = DecisionTreeClassifier(max_depth = 3, random_state = 42)

# Train model

dtc.fit(X_train, y_train)

# Model prediction

dtc_pred = dtc.predict(X_test)

# Error metric

print(confusion_matrix(y_test, dtc_pred))

print(classification_report(y_test, dtc_pred))

# Confirm coefficient

dtc.feature_importances_

# Plot

plt.bar(X_train.columns, dtc.feature_importances_)

plt.figure(figsize=(8, 8))

plot_tree(dtc,

         feature_names = iris['feature_names'],

         class_names = iris['target_names'],

         filled = True

);