Classification using Tree Models

#binary #classification #decission #ensamble #forest #learning #machine #ml #model #notes #python #sklearn #tensorflow #tree

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
import xgboost as xgb
from catboost import CatBoostClassifier
import lightgbm as lgb
from sklearn.metrics import accuracy_score, classification_report
import matplotlib.pyplot as plt

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Decision Tree
dt_classifier = DecisionTreeClassifier(random_state=42)
dt_classifier.fit(X_train, y_train)
dt_predictions = dt_classifier.predict(X_test)
dt_accuracy = accuracy_score(y_test, dt_predictions)
print("Decision Tree Accuracy:", dt_accuracy)

# Gradient Boosting
gb_classifier = GradientBoostingClassifier(n_estimators=100, random_state=42)
gb_classifier.fit(X_train, y_train)
gb_predictions = gb_classifier.predict(X_test)
gb_accuracy = accuracy_score(y_test, gb_predictions)
print("Gradient Boosting Accuracy:", gb_accuracy)

# XGBoost
xgb_classifier = xgb.XGBClassifier(random_state=42)
xgb_classifier.fit(X_train, y_train)
xgb_predictions = xgb_classifier.predict(X_test)
xgb_accuracy = accuracy_score(y_test, xgb_predictions)
print("XGBoost Accuracy:", xgb_accuracy)

# CatBoost
catboost_classifier = CatBoostClassifier(random_state=42, verbose=False)
catboost_classifier.fit(X_train, y_train)
catboost_predictions = catboost_classifier.predict(X_test)
catboost_accuracy = accuracy_score(y_test, catboost_predictions)
print("CatBoost Accuracy:", catboost_accuracy)

# LightGBM
lgb_classifier = lgb.LGBMClassifier(random_state=42)
lgb_classifier.fit(X_train, y_train)
lgb_predictions = lgb_classifier.predict(X_test)
lgb_accuracy = accuracy_score(y_test, lgb_predictions)
print("LightGBM Accuracy:", lgb_accuracy)

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