Even though it has an ornate name, the underlying concept is actually quite simple and intuitive. The concept of Empirical Risk Minimization becomes relevant in the world of supervised learning. The actual goal of supervised learning is to find a model that solves a problem as opposed to finding a model that best fits the … Continue reading What is Empirical Risk Minimization
Tag: Supervised Learning
Measuring the Stability of Machine Learning Algorithms
When you think of a machine learning algorithm, the first metric that comes to mind is its accuracy. A lot of research is centered on developing algorithms that are accurate and can predict the outcome with a high degree of confidence. During the training process, an important issue to think about is the stability of … Continue reading Measuring the Stability of Machine Learning Algorithms
How To Compute Confidence Measure For SVM Classifiers
Support Vector Machines are machine learning models that are used to classify data. Let's say you want to build a system that can automatically identify if the input image contains a given object. For ease of understanding, let's limit the discussion to three different types of objects i.e. chair, laptop, and refrigerator. To build this, … Continue reading How To Compute Confidence Measure For SVM Classifiers
Dissecting Bias vs. Variance Tradeoff In Machine Learning
I was recently working on a machine learning problem when I stumbled upon an interesting question. I wanted to build a machine learning model using a labeled dataset that can classify an unknown image. It's a classic supervised learning problem! I was not exactly sure how the model would turn out, so I had to … Continue reading Dissecting Bias vs. Variance Tradeoff In Machine Learning
Overfitting In Machine Learning
Let's say you are given a small set of data points. These data points can take any form like weight distribution of people, location of people who buy your products, types of smartphones, etc. Now your job is to estimate the underlying model. As in, if an unknown point comes in, you should to be … Continue reading Overfitting In Machine Learning
Support Vector Machines
In machine learning, we have supervised learning on one end and unsupervised learning on the other end. Support Vector Machines (SVMs) are supervised learning models used to analyze and classify data. We use machine learning algorithms to train the machines. Once we have a model, we can classify unknown data. Let's say you have a … Continue reading Support Vector Machines
Perpetual Enigma 