• MLG 007 Logistic Regression

  • Feb 19 2017
  • Length: 35 mins
  • Podcast

MLG 007 Logistic Regression

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  • Summary

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    Full notes at ocdevel.com/mlg/7. See Andrew Ng Week 3 Lecture Notes

    Overview
    • Logistic Function: A sigmoid function transforming linear regression output to logits, providing a probability between 0 and 1.
    • Binary Classification: Logistic regression deals with binary outcomes, determining either 0 or 1 based on a threshold (e.g., 0.5).
    • Error Function: Uses log likelihood to measure the accuracy of predictions in logistic regression.
    • Gradient Descent: Optimizes the model by adjusting weights to minimize the error function.
    Classification vs Regression
    • Classification: Predicts a discrete label (e.g., a cat or dog).
    • Regression: Predicts a continuous outcome (e.g., house price).
    Practical Example
    • Train on a dataset of house features to predict if a house is 'expensive' based on labeled data.
    • Automatically categorize into 0 (not expensive) or 1 (expensive) through training and gradient descent.
    Logistic Regression in Machine Learning
    • Neurons in Neural Networks: Act as building blocks, as logistic regression is used to create neurons for more complex models like neural networks.
    • Composable Functions: Demonstrates the compositional nature of machine learning algorithms where functions are built on other functions (e.g., logistic built on linear).
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