Logistic Regression, Machine Learning Crash Course, Google Developers

Logistic Regression. Instead of predicting exactly 0 or 1, logistic regression generates a probability—a value between 0 and 1, exclusive. For example, consider a logistic regression model for spam detection. If the model infers a value of 0.932 on a particular email message, it implies a 93.2% probability that the email message is spam. More precisely, it means that in the limit of infinite training examples, the set of examples for which the model predicts 0.932 will actually be spam 93.2% of the time and the remaining 6.8% will not. Logistic Regression. Predicting Coin Flips? Imagine the problem of predicting probability of Heads for bent coins You might use features like angle of bend, coin mass, etc. What's the simplest model you could use? What could go wrong? Logistic Regression. Many problems require a probability estimate as output Enter Logistic Regression. Logistic Regression. Many problems require a probability estimate as output Enter Logistic Regression Handy because the probability estimates are calibrated for example, p(house will sell) * price = expected outcome. Logistic Regression. Many problems require a probability estimate as output Enter Logistic Regression Handy because the probability estimates are calibrated for example, p(house will sell) * price = expected outcome Also useful for when we need a binary classification spam or not spam? → p(Spam) Logistic Regression -- Predictions. LogOdds (Sum of x i * w i + b) LogLoss Defined. $$ LogLoss = \sum_ -y\,log(y') - (1 - y)\,log(1 - y') $$ Logistic Regression and Regularization. Regularization is super important for logistic regression. Remember the asymptotes It'll keep trying to drive loss to 0 in high dimensions. Logistic Regression and Regularization. Regularization is super important for logistic regression. Remember the asymptotes It'll keep trying to drive loss to 0 in high dimensions Two strategies are especially useful: L 2 regularization (aka L 2 weight decay) - penalizes huge weights. Early stopping - limiting training steps or learning rate. Linear Logistic Regression. Linear logistic regression is extremely efficient. Very fast training and prediction times. Short / wide models use a lot of RAM. Voice dubbing for this video lecture was generated using machine learning techniques. Please help us to refine our voice dubbing technology; click Send Feedback above to submit bug reports and suggestions. To change to English audio, choose English from the drop-down at the bottom-left of the page. Video not displaying? Please see the community page for troubleshooting assistance.