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Logistic Regression in Python - ML From Scratch 03

In this Machine Learning from Scratch Tutorial, we are going to implement the Logistic Regression algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

Further readings:

Implementation

import numpy as np class LogisticRegression: def __init__(self, learning_rate=0.001, n_iters=1000): self.lr = learning_rate self.n_iters = n_iters self.weights = None self.bias = None def fit(self, X, y): n_samples, n_features = X.shape # init parameters self.weights = np.zeros(n_features) self.bias = 0 # gradient descent for _ in range(self.n_iters): # approximate y with linear combination of weights and x, plus bias linear_model = np.dot(X, self.weights) + self.bias # apply sigmoid function y_predicted = self._sigmoid(linear_model) # compute gradients dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y)) db = (1 / n_samples) * np.sum(y_predicted - y) # update parameters self.weights -= self.lr * dw self.bias -= self.lr * db def predict(self, X): linear_model = np.dot(X, self.weights) + self.bias y_predicted = self._sigmoid(linear_model) y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted] return np.array(y_predicted_cls) def _sigmoid(self, x): return 1 / (1 + np.exp(-x))

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