Machine Learning Algorithm - Logistic Regression
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Logistic regression is a classification algorithm used to predict binary outcomes. It transforms predictor variable values using the sigmoid function to produce a probability value between 0 and 1. The log odds of the outcome are modeled as a linear combination of the predictor variables. Positive coefficient values increase the probability of the outcome while negative values decrease the probability. Logistic regression outputs probabilities that can be converted into binary class predictions.
![Sigmoid Function Refresher
The logistic function or a Sigmoid function is a function which takes any real value between zero and one. It is
defined as
y = 1 / (1 + e-x)
where y is the scaled value and x is the input value.
The logistic function transforms the original range of [-,] to [0,1] and also has a linear part on the transform.](https://image.slidesharecdn.com/lesson15ml-logisticregression-200627082721/85/Machine-Learning-Algorithm-Logistic-Regression-3-320.jpg)
Machine Learning Algorithm - Logistic Regression
- 1. Lesson 12 Machine Learning – Logistic Regression Kush Kulshrestha
- 2. Introduction • One of the most simple algorithm used for 2 class classification problem. • Logistic Regression can be used for various classification problems such as spam detection. Diabetes prediction, if a given customer will purchase a particular product or will they churn another competitor, whether the user will click on a given advertisement link or not. • Just like Linear Regression uses optimize Least Square function to calculate the coefficients, Logistic Regression uses Log Likelihood function to find the value of the best fit parameters. • Logistic Regression is the basic algorithm used to define a Neural Network.
- 3. Sigmoid Function Refresher The logistic function or a Sigmoid function is a function which takes any real value between zero and one. It is defined as y = 1 / (1 + e-x) where y is the scaled value and x is the input value. The logistic function transforms the original range of [-,] to [0,1] and also has a linear part on the transform.
- 4. Probability, Odds and Log odds Probability of an event = (No of Success / Total no of cases) Odds = (One outcome / all other outcomes) Examples: Conversion: • For rolling a dice and getting 1 – Probability = (1/6) Odds = (1/5) • Rolling a dice and getting an even number – Probability = (3/6) = 0.5 Odds = (3/3) = 1 • Dice roll less than 5 – Probability = (4/6) = (2/3) Odds = (4/2) = 2
- 5. What is Logistic Regression Linear regression is continuous response is modelled as a linear combination of the features. In Logistic regression, log-odds of a categorical response being "true" (1) is modelled as a linear combination of the features. This is called the logit function. The equation can be rearranged into the logistic function: p = Conclusion – • Logistic regression outputs the probabilities of a specific class. • Those probabilities can be converted into class predictions. • The logistic function takes on an "s" shape and is bounded by 0 and 1.
- 6. Interpreting Logistic Regression Coefficients Final equation of Logistic regression is: Interpreting beta: ‘1’ unit increase in ‘x' is associated with a B1 unit increase in the log-odds of event for which p is probability of. Log odds are: Hence, positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability).