Olá, resolvi o exercício da seguinte forma:
Importando bibliotecas necessárias
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
Carregando os dados do Dataset Iris
data_dataset = load_iris()
Gerando os dados no Pandas
df = pd.DataFrame(data=data_dataset.data, columns=data_dataset.feature_names)
df['species'] = data_dataset.target
df.head()
Separando atributos e labels e trabalhando com o conjunto
x = df[data_dataset.feature_names]
y = df['species']
Divisão do treino e testes
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)
Treinar o LLM no modelo Decision Tree
tree_model = DecisionTreeClassifier()
tree_model.fit(X_train, y_train)
Avaliando o modelo
y_pred = tree_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Acurácia do modelo: {accuracy * 100:.2f}%")
Treinar o LLM no modelo KNN - KNeighborsClassifier
knn_model = KNeighborsClassifier()
knn_model.fit(X_train, y_train)
knn_accuracy = accuracy_score (y_test, knn_model.predict(X_test))
print(f"Acurácia do modelo: {accuracy * 100:.2f}%")
