TIL - 06.12

초안.

https://double-d.tistory.com/23

# 선형회귀 훈련(적합)
from sklearn.linear_model import LinearRegression
model_lr = LinearRegression()
type(model_lr)

sns.get_dataset_names()
iris = sns.load_dataset('iris')

model_lr.fit(X = X_1, y = y_1)

# 가중치(w1)
print(model_lr.coef_)
# 편향(bias, w0)
print(model_lr.intercept_)

w1 = model_lr.coef_[0][0]
w0 = model_lr.intercept_[0]

print('y = {}x + {}'.format(w1.round(2),w0.round(2)))

 

 

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn 

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression


LogisticRegression = sklearn.linear_model.LogisticRegression

model_lor = LogisticRegression()

sns.get_dataset_names()
iris = sns.load_dataset('iris')

def get_att(x):
    #x모델을 넣기
    print('클래스 종류', x.classes_)
    print('독립변수 갯수', x.n_features_in_)
    print('들어간 독립변수(x)의 이름',x.feature_names_in_)
    print('가중치',x.coef_)
    print('바이어스', x.intercept_)
    

get_att(model_lor)

from sklearn.metrics import accuracy_score, f1_score
def get_metrics(true, pred):
    print('정확도', accuracy_score(true, pred))
    print('f1-score', f1_score(true, pred))


X_2 = iris[['sepal_length','sepal_width','petal_length','petal_width']]
y_2 = iris['species']


model_lor = LogisticRegression()
model_lor.fit(X_2, y_2)

# Print the classes
print(f"Classes: {model_lor.classes_}")


y_pred_2 = model_lor.predict(X_2)


get_metrics(y_2, y_pred_2)

 

 

 

# 필요한 라이브러리 임포트
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

# 1. 데이터셋 로드
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 2. 데이터 전처리
# 데이터셋을 훈련 세트와 테스트 세트로 분리
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# # 필요한 경우 데이터 정규화
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 3. 모델 생성
logistic_regression_model = LogisticRegression(max_iter=200)

# 4. 모델 훈련
logistic_regression_model.fit(X_train, y_train)

# 5. 모델 평가
y_pred = logistic_regression_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f'Accuracy: {accuracy}')
print(f'Classification Report:\n{report}')

# 6. 예측 수행
# 새로운 데이터에 대해 예측을 수행할 수 있습니다.
new_data = [[5.1, 3.5, 1.4, 0.2]]  # 예시 데이터
new_data_scaled = scaler.transform(new_data)
prediction = logistic_regression_model.predict(new_data_scaled)
predicted_class = iris.target_names[prediction[0]]
print(f'Predicted class: {predicted_class}')