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Logistic Regression in python

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Space Cadet answered on May 23, 2020 Popularity 10/10 Helpfulness 2/10

answer Logistic Regression in python

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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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logistic regression algorithm in python

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# import the class
from sklearn.linear_model import LogisticRegression

# instantiate the model (using the default parameters)
logreg = LogisticRegression()

# fit the model with data
logreg.fit(X_train,y_train)

#
y_pred=logreg.predict(X_test)

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Contributed on May 23 2020

Wide-eyed Whale

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logistic regression python

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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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logistic regression algorithm in python

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# import the metrics class
from sklearn import metrics
cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
cnf_matrix

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Contributed on May 23 2020

Wide-eyed Whale

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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

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- It is a classification problem
- It is called regression because it calculates continuous value (sigmoid / likelihood /probability) 
- It compares threshold value with probability to reach conclusion 
- It works well for data with 2 class that is linearly separable by a line (decision boundary)
- For multiple classes, use softmax
- Logistic regression only calculates probability of positive (target) class only. subtracting from 1 will give us probability of negative class
- Training process:
    1. Initializes theta (co-efficients) with random values
    2. Calculate sigmoid (probability) of output for a case
    3. compare this probability with actual output and record this difference as error
    4. Calculate this error for all training cases. Total error is the cost of the model (known as cost function. eg: MSE)
    5. Change theta in such a way that it reduces total cost (optimization algorithm like gradient descent)
    6. Iterate from step 2 until the model is satisfactory enough with low cost.

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Contributed on Feb 13 2023

Innocent Iguana

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Logistic Regression in python

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