Natural Language Processing(Part 10)-Logistic Regression: Training
📚Chapter 2: Sentiment Analysis (Logistic Regression)
Logistic Regression: Training
In the previous tutorial, you learned how to classify whether a tweet has a positive sentiment or negative sentiment, using a theta that I have given you. In this tutorial, you will learn your own theta from scratch, and specifically, I’ll walk you through an algorithm that allows you to get your theta variable.
Let’s see how you can do this. To train your logistic regression classifier, iterate until you find the set of parameters theta, that minimizes your cost function. Let us suppose that your loss only depends on the parameters theta1 and theta2, you would have a cost function that looks like this contour plots on the left. On the right, you can see the evolution of the cost function as you iterate. First, you would have to initialize your parameters theta. Then you will update your theta in the direction of the gradient of your cost function. After a 100 iterations, you would be at this point, after 200 here, and so on. After many iterations, you derive to a point near your optimum costs and you’d end your training here.
Let’s look at this process in more detail. First, you’d have to initialize your parameters vector theta. Then you’d use the logistic function to get values for each of your observations. After that, you’d be able to calculate the gradients of your cost function and update your parameters. Finally, you’d be able to compute your cost J and determine if more iterations are needed according to a stop-parameter or maximum number of iterations. As you might have seen in the other courses, this algorithm is known as gradient descent. Now, that you have your theta variable, you want to evaluate your theta, meaning you want to evaluate your classifier. Once you put in your theta into your sigmoid function, do get a good classifier or do you get a bad classifier? In the next tutorial, we will show you how you can do this.
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References
1- Natural Language Processing with Classification and Vector Spaces
