#Logistic regression
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score

#Load iris data
data = load_iris()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.DataFrame(data.target, columns=["Species"])
df #Display iris data

#Creating, learning, and predicting logistic regression models
model = LogisticRegression()
model.fit(X, y)
y_pred = model.predict(X)
print(y_pred) #Predicted results for 150 iris data

#Model evaluation
print(mean_squared_error(y, y_pred)) #Mean squared error. The smaller the better
print(r2_score(y, y_pred)) #Coefficient of determination. The larger the value between 0 and 1, the better.

Recommended Posts