Decision Tree Algorithm

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
 
#Creating a decision tree
from sklearn.datasets import make_blobs
 
X, y = make_blobs(n_samples=300, centers=4,
                  random_state=0, cluster_std=1.0)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');
plt.show()
 
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier().fit(X, y)
 
def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):
    ax = ax or plt.gca()
 
    # Plot the training points
    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,
               clim=(y.min(), y.max()), zorder=3)
    ax.axis('tight')
    ax.axis('off')
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
 
    # fit the estimator
    model.fit(X, y)
    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),
                         np.linspace(*ylim, num=200))
    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
 
    # Create a color plot with the results
    n_classes = len(np.unique(y))
    contours = ax.contourf(xx, yy, Z, alpha=0.3,
                           levels=np.arange(n_classes + 1) - 0.5,
                           cmap=cmap, clim=(y.min(), y.max()),
                           zorder=1)
 
    ax.set(xlim=xlim, ylim=ylim)
 
visualize_classifier(DecisionTreeClassifier(), X, y)
 
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier
 
tree = DecisionTreeClassifier()
bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,
                        random_state=1)
 
bag.fit(X, y)
visualize_classifier(bag, X, y)
 

import matplotlib.pyplot as plot
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
 
iris = datasets.load_iris()
X = iris.data[:, 2:]
Y = iris.target
 
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5, random_state=1, stratify=Y)
 
classifier_tree = DecisionTreeClassifier(criterion='gini', max_depth=6, random_state=1)
classifier_tree.fit(X_train, Y_train)
 
figure, axis = plot.subplots(figsize=(12, 12))
tree.plot_tree(classifier_tree, fontsize=12)
plot.show()
 

import numpy as np
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plot
 
 
range = np.random.RandomState(1)
X = np.sort(5 * range.rand(80, 1), axis=0)
Y = np.sin(X).ravel()
Y[::5] += 3 * (0.5 - range.rand(16))
 
 
regression_1 = DecisionTreeRegressor(max_depth=2)
regression_2 = DecisionTreeRegressor(max_depth=5)
regression_1.fit(X, Y)
regression_2.fit(X, Y)
 
 
X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
Y1 = regression_1.predict(X_test)
Y2 = regression_2.predict(X_test)
 
 
plot.figure()
plot.scatter(X, Y, s=20, edgecolor="black", c="pink", label="data")
plot.plot(X_test, Y1, color="blue", label="max_depth=4", linewidth=2)
plot.plot(X_test, Y2, color="green", label="max_depth=7", linewidth=2)
plot.xlabel("data")
plot.ylabel("target")
plot.title("Decision Tree Regression")
plot.legend()
plot.show()