sminejuno — 2023-11-01 11:33:50 UTC

import numpy as np
import pandas as pd
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
wine = load_wine()
X = wine.data
y = wine.target

# 데이터를 훈련 세트와 테스트 세트로 분할
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 선형 회귀 모델 생성 및 훈련
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)

# 의사결정 트리 회귀 모델 생성 및 훈련
tree_model = DecisionTreeRegressor()
tree_model.fit(X_train, y_train)

# 랜덤 포레스트 회귀 모델 생성 및 훈련
rf_model = RandomForestRegressor()
rf_model.fit(X_train, y_train)

# 예측
y_pred_linear = linear_model.predict(X_test)
y_pred_tree = tree_model.predict(X_test)
y_pred_rf = rf_model.predict(X_test)

# 모델 평가
mse_linear = mean_squared_error(y_test, y_pred_linear)
r2_linear = r2_score(y_test, y_pred_linear)

mse_tree = mean_squared_error(y_test, y_pred_tree)
r2_tree = r2_score(y_test, y_pred_tree)

mse_rf = mean_squared_error(y_test, y_pred_rf)
r2_rf = r2_score(y_test, y_pred_rf)

print("Linear Regression - Mean Squared Error (MSE):", mse_linear)
print("Linear Regression - R-squared (R2) Score:", r2_linear)

print("Decision Tree Regression - Mean Squared Error (MSE):", mse_tree)
print("Decision Tree Regression - R-squared (R2) Score:", r2_tree)

print("Random Forest Regression - Mean Squared Error (MSE):", mse_rf)
print("Random Forest Regression - R-squared (R2) Score:", r2_rf)

sminejuno — 2023-11-01 11:49:27 UTC

data = load_iris() df = pd.DataFrame(data.data, columns=data.feature_names) print(df)

2023-11-01 11:52:48 UTC

from sklearn.datasets import load_iris
import pandas asd pd


data = Ioad_iris() 
df = pd.DataFrame(data.data, columns=data.feature_names) 
print(df)
X = pd.DataFrame(data.data, columns=data.feature_names)
y = data.target


from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

import matplotlib.pyplot as plt

plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y)
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.sho

나의 시대를 휩쓰는 Padlet by 서민준