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logistic regression sklearn

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VIvek answered on June 1, 2022 Popularity 10/10 Helpfulness 2/10

answer logistic regression sklearn

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logistic regression sklearn

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LogisticRegression(penalty='l2',C=1.0, fit_intercept=True, max_iter=100)

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Contributed on Jun 01 2022

VIvek

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from sklearn.metrics import confusion_matrix
# Specify independent and dependent features
X = np.asarray(df[['A', 'B', 'C', 'D', 'E', 'F', 'G']])
y = np.asarray(df['target'])

# Preprocess dataset
from sklearn import preprocessing
X = preprocessing.StandardScaler().fit(X).transform(X)

# Split into train and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)

# Train the model
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression(C=0.01, solver='liblinear')
LR.fit(X_train,y_train)

# Predict the test set
y_pred = LR.predict(X_test)

# See classification report and confusion matrix
from sklearn.metrics import classification_report, confusion_matrix
classification_report(y_test, y_pred)
confusion_matrix(y_test, y_pred, labels=[1,0])

# Predicted probability on test set for positive/target class
y_pred_prob = LR.predict_proba(X_test)[:, 1]

# Evaluate the model
from sklearn.metrics import jaccard_score
jaccard_score(y_test, y_pred,pos_label=0)

from sklearn.metrics import log_loss
log_loss(y_test, y_pred_prob)

from sklearn.metrics import roc_auc_score
print(roc_auc_score(y_test, y_pred_prob))

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Contributed on Feb 13 2023

Innocent Iguana

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python logistic regression

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from statsmodels.formula.api import logit
model = logit("target ~ x_var", data=df).fit()
print(model.params)
# Visualize logistic model
sns.regplot(x="x_var", y="target", data=df, ci=None, logistic=True)
X_test = pd.DataFrame({"x_var": np.arange(-1, 6.25, 0.25)})
y_pred_prob = model.predict(X_test)
y_pred = np.round(y_pred_prob)
# Odds ratio : p/(1-p) or probability of something happenning over not happening
odds_ratio = y_pred_prob / (1- y_pred_prob)
# Visualize odds ratio / log odds ratio
sns.lineplot(x="x_var", y="odds_ratio", data=df)
plt.axhline(y=1, linestyle="dotted")
plt.yscale("log") # If you want to make the curve into linear make y : np.log(odds_ratio)
plt.show()
# Confusion matrix
conf_matrix = model.pred_table()
TN = conf_matrix[0,0]
TP = conf_matrix[1,1]
FN = conf_matrix[1,0]
FP = conf_matrix[0,1]
# Visualize confusion matrix
from statsmodels.graphics.mosaicplot import mosaic
mosaic(conf_matrix)

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Contributed on Dec 23 2023

Innocent Iguana

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Logistic Regression in python

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import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=10000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None
    
    def fit(self, X, y):
        # initialize weights and bias to zero
        self.weights = np.zeros(X.shape[1])
        self.bias = 0
        
        # gradient descent
        for i in range(self.num_iterations):
            z = np.dot(X, self.weights) + self.bias
            y_pred = self.sigmoid(z)
            
            # calculate gradients
            dw = (1 / X.shape[0]) * np.dot(X.T, (y_pred - y))
            db = (1 / X.shape[0]) * np.sum(y_pred - y)
            
            # update weights and bias
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db
    
    def predict(self, X):
        z = np.dot(X, self.weights) + self.bias
        y_pred = self.sigmoid(z)
        return np.round(y_pred)
    
    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

more on : https://pieriantraining.com/machine-learning-with-python-logistic-regression-for-binary-classification/

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Contributed on Apr 13 2023

Space Cadet

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