Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post, you will discover the logistic regression algorithm for machine learning. After reading this post you will know: The many names and terms used when […]
Regression models are fit on training data using linear regression and local search optimization algorithms. Models like linear regression and logistic regression are trained by least squares optimization, and this is the most efficient approach to finding coefficients that minimize error for these models. Nevertheless, it is possible to use alternate optimization algorithms to fit […]
There is an important difference between classification and regression problems. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. I often see questions such as: How do I calculate accuracy for my regression problem? Questions like this are a symptom of not truly understanding the difference between classification and regression […]
The fastest way to get good at applied machine learning is to practice on end-to-end projects. In this post you will discover how to work through a regression problem in Weka, end-to-end. After reading this post you will know: How to load and analyze a regression dataset in Weka. How to create multiple different transformed […]
Machine learning is an amazing tool for many tasks. OpenCV is a great library for manipulating images. It would be great if we can put them together. In this 7-part crash course, you will learn from examples how to make use of machine learning and the image processing API from OpenCV to accomplish some goals. […]
R is a programming language of its kind. It is a language for statistics, and its ecosystem has a lot of libraries for all kinds of statistical tasks. It is a language targeted the statisticians rather than computer scientists. Hence you will see some unorthodox patterns in the language. In this post, you will learn […]
The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this […]
While a logistic regression classifier is used for binary class classification, softmax classifier is a supervised learning algorithm which is mostly used when multiple classes are involved. Softmax classifier works by assigning a probability distribution to each class. The probability distribution of the class with the highest probability is normalized to 1, and all other […]
In order to build a classifier that accurately classifies the data samples and performs well on test data, you need to initialize the weights in a way that the model converges well. Usually we randomized the weights. But when we use mean square error (MSE) as loss for training a logistic regression model, we may […]
The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this […]
