{
  "id": 13445229,
  "title": "Logistic Regression",
  "url": "https://urgent.news/2026/10/10/logistic-regression",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-10T15:26:14.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/jubito27/logistic-regression-4oij"
  },
  "original_language": "en",
  "account": "Logistic Regression is a foundational algorithm in Machine Learning, typically learned after Linear Regression. While Linear Regression draws a straight line to predict continuous values, Logistic Regression is used for binary classification problems, such as predicting whether a student is Placed (1) or Not Placed (0) based on their exam scores. Linear Regression struggles with binary data because the line extends infinitely, potentially predicting impossible values. Logistic Regression addresses this by squishing the output into an S-shaped curve between 0 and 1, yielding a probability value (like an 85% chance of being placed).\n\nThe training process begins with initializing weights and biases. For n features, n weights and a bias term are needed. In our example with two features (CGPA and IQ), three weights are required. Initially, all weights are set to zero, and the bias is set to zero as well. The model then calculates a raw linear score (z) for each student using its current weights: z = bias + (w1 × CGPA) + (w2 × IQ). With all weights at zero, Student 1 receives a score of z = 0. This score is then passed through the Sigmoid function to convert it into a probability (p): p = 1 / (1 + e^-z). In the case of z = 0, the probability is 0.5, representing a 50% chance of being placed.\n\nThe quality of this guess is evaluated using Binary Cross-Entropy (BCE) Loss, a formula that measures the dissimilarity between the actual and predicted probabilities for each data point in the dataset.",
  "summary": "When you start your journey in Machine Learning, the first algorithm you usually learn is Linear Regression. Linear regression draws a straight line using the equation: y = m x + c This straight line is amazing for predicting continuous numbers, like the price of a house or a student's score. But what happens if you want to predict a category? For example, predicting whether a student is Placed (…",
  "key_points": [
    "Logistic Regression is foundational in Machine Learning, learned post Linear Regression",
    "Predicts binary outcomes like student placement (1) or not (0) based on exam scores",
    "Converts raw scores into probabilities between 0 and 1 using Sigmoid function"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}