## The Simpler Derivation of Logistic Regression

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Logistic regression is derivative of binary logistic regression results of the most popular ways to fit models for categorical data, especially for binary response data.

It is the most important and probably most used member of a class of models called generalized linear models. Unlike linear regression, logistic regression can directly predict probabilities values that are restricted to the 0,1 interval ; furthermore, those probabilities are well-calibrated when compared to the probabilities predicted by some other classifiers, such as Naive Bayes.

Logistic regression preserves the marginal probabilities of the training data. The coefficients of the model also provide some hint of the relative importance of each input variable. Unfortunately, most derivations like derivative of binary logistic regression results ones in [Agresti, ] or [Hastie, et. To make the discussion easier, we will focus on the binary response case.

The logistic regression model assumes that the log-odds of an observation y can be expressed as derivative of binary logistic regression results linear function of the K input variables x:.

The left hand side of the above equation is called the logit of P hence, the name logistic regression. This immediately tells us that logistic models are multiplicative in their inputs rather than additive, like a linear modeland it gives us a way to interpret the coefficients.

If x j is a binary variable say, sex, with female coded as 1 and male as 0then if the subject is female, then the response is two times more likely to be true than if the subject is male, all other things being equal. The right hand side of the top equation is the sigmoid of zwhich maps the real line to the interval 0, 1and is approximately linear near the origin.

Later, we will want to take the gradient of P with respect to the set of coefficients brather than z. The solution to a Logistic Regression problem is the set of parameters b that maximizes the likelihood of the data, which is expressed as the product of the predicted probabilities of the N individual observations. Maximizing the log-likelihood will maximize the likelihood. It is analogous to the residual sum of squares RSS of a linear model. Ordinary least squares minimizes RSS; logistic regression minimizes deviance.

A useful goodness-of-fit heuristic for a logistic regression model is to compare the deviance of the model with the so-called null deviance: One minus the ratio of deviance to null deviance is sometimes called pseudo-R 2and is used the way one would use R 2 to evaluate a linear model.

Traditional derivations of Logistic Regression tend to start by substituting the logit function directly into the log-likelihood equations, and expanding from there. To maximize the log-likelihood, we take its gradient with respect to b:. The maximum occurs where the gradient is zero. We can now cancel terms and set the gradient to zero. This gives us the set of simultaneous equations that are derivative of binary logistic regression results at the optimum:.

Notice that the equations to be solved are in terms of the probabilities P which are a function of bnot directly in terms of the coefficients b themselves. This means that logistic models derivative of binary logistic regression results coordinate-free: Only the values of the coefficients will change.

The other thing to notice from the above equations is that the sum of probability mass across each coordinate of the x i vectors is equal to the count of observations with that coordinate value for which the response was true.

For example, suppose the jth input variable is 1 if the subject is female, 0 if the subject is male.

This is what we mean when we say that logistic regression preserves the marginal probabilities of the training data. Suppose you have a vector valued function f: Assuming that we start with an initial guess b 0we can take the Taylor expansion of f around b In our case, f derivative of binary logistic regression results the gradient of the log-likelihood, and its Jacobean is the Hessian the matrix of second derivatives of the log-likelihood function.

This is why the technique for solving logistic regression problems is sometimes referred to as iteratively re-weighted least squares. Generally, the method does not take long to converge about 6 or so iterations. Thinking of logistic regression as a weighted least squares problem immediately tells you a few things that can go wrong, and how. For example, if some of the input variables are correlated, then the Hessian H will be ill-conditioned, or even singular.

It can also result in coefficients with excessively large magnitudes, and often the wrong sign. If an input perfectly predicts the response for some subset of the data at no penalty on the rest of the datathen the term P i 1 — P i will be driven to zero for that subset, which will drive the coefficient for that input to infinity if the input perfectly predicted all the data, then the residual y — P k has already gone to zero, which means that you are already at the optimum.

On the other hand, the least squares analogy also gives us the solution to these problems: Regularized regression penalizes derivative of binary logistic regression results large derivative of binary logistic regression results, and keeps them bounded.

If you are implementing your own logistic regression procedure, rather than using a package, then it is straightforward to implement a regularized least squares for the iteration step as Win-Vector has done. But even if you are using an off-the-shelf implementation, the above discussion will help give you a sense of how to interpret derivative of binary logistic regression results coefficients of your model, and how to recognize and troubleshoot some issues that might arise.

Here is what you should now know from going through the derivation of logistic regression step by step:. Or put another way, it could be a sign that this input is only really useful on a subset of your data, so perhaps it is time to segment the data.

The Elements of Statistical Learning, 2nd Edition. Clearest derivation of LR that I have come across. Rama Great suggestion about the decision tree. Thanks for your comments.

This form is more common in the MLP literature, and is a little easier to deal with sometimes because z appears only once. The logistic regression model assumes that the log-odds of an observation y can be expressed as a linear function of the K input variables x: Oh, one other thing. Win-Vector starts submitting content to r-bloggers. The equivalence of logistic regression and maximum entropy models.

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Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Unlike linear regression which outputs continuous number values, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes. Given data on time spent studying and exam scores. Linear Regression and logistic regression can predict different things:. We have two features hours slept, hours studied and two classes: In order to map predicted values to probabilities, we use the sigmoid function.

The function maps any real value into another value between 0 and 1. In machine learning, we use sigmoid to map predictions to probabilities. Our current prediction function returns a probability score between 0 and 1.

For example, if our threshold was. If our prediction was. For logistic regression with multiple classes we could select the class with the highest predicted probability.

Using our knowledge of sigmoid functions and decision boundaries, we can now write a prediction function. As the probability gets closer to 1, our model is more confident that the observation is in class 1. This time however we will transform the output using the sigmoid function to return a probability value between 0 and 1. If the model returns.

If our decision boundary was. We wrap the sigmoid function over the same prediction function we used in multiple linear regression. Squaring this prediction as we do in MSE results in a non-convex function with many local minimums.

If our cost function has many local minimums, gradient descent may not find the optimal global minimum. Cross-entropy loss can be divided into two separate cost functions: These smooth monotonic functions [7] always increasing or always decreasing make it easy to calculate the gradient and minimize cost.

The key thing to note is the cost function penalizes confident and wrong predictions more than it rewards confident and right predictions! The corollary is increasing prediction accuracy closer to 0 or 1 has diminishing returns on reducing cost due to the logistic nature of our cost function. In both cases we only perform the operation we need to perform.

To minimize our cost, we use Gradient Descent just like before in Linear Regression. Machine learning libraries like Scikit-learn hide their implementations so you can focus on more interesting things!

One of the neat properties of the sigmoid function is its derivative is easy to calculate. Michael Neilson also covers the topic in chapter 3 of his book. Notice how this gradient is the same as the Mean Squared Error gradient, the only difference is the hypothesis function. Our training code is the same as we used for linear regression. Accuracy measures how correct our predictions were. In this case we simple compare predicted labels to true labels and divide by the total.

Another helpful technique is to plot the decision boundary on top of our predictions to see how our labels compare to the actual labels. This involves plotting our predicted probabilities and coloring them with their true labels.

Basically we re-run binary classification multiple times, once for each class. Then we take the class with the highest predicted value. Linear Regression and logistic regression can predict different things: Linear regression predictions are continuous numbers in a range. Logistic Regression could help use predict whether the student passed or failed.

Logistic regression predictions are discrete only specific values or categories are allowed. Studied Slept Passed 4. Math One of the neat properties of the sigmoid function is its derivative is easy to calculate. Calculate gradient average 2. Multiply by learning rate 3. For each class… Predict the probability the observations are in that single class.