Sam Jang's Blog

Ridge and Lasso Regression Hello! Today we will be exploring two different regression algorithms called Ridge Regression and Lasso Regression which are similar to how Linear Regression works.  Before we get into how Lasso and Ridge regression models work, you need to understand what is meant by the term 'overfitting'. Overfitting: Overfitting refers to training the model too well by the training data.  For example, the indicator that your model is overfitting could be when the total cost of your model by the training data is zero and the cost for your test data is huge. As a result, this could negatively affect your model as it's unable to predict accurately for your testing data. I will be exploring more about the concept of overfitting and underfitting in future posts Ridge Regression: Like I said at the beginning of this post, the Lasso regression model is very similar to how the Linear regression model works. As some of you may already know...

K Nearest Neighbors Hello! In this post, we will be exploring a very simple classification algorithm called 'K Nearest Neighbors'. How K Nearest Neighbors work: The green point above is the testing data and the blue and red points are training data in different classes. The way KNN works is by taking the Euclidean distance from your test data(green point) to each of your training data and classify your test data by the class of the nearest point to the testing data. Euclidean Distance: The Euclidean distance is basically the distance between two points on the Euclidean space. To put simply, the distance between two points.  Formula : K K: Parameter that takes a certain number of nearest points to use for the classification process.  K basically takes 'k' number of nearest points(training data) and classifies the test data by the class that has the highest vote. For example, if ...

Classification vs Regression Classification: Classification algorithms attempt to predict a discrete class label. For example, a problem where you are trying to build a model(or algorithm) to interpret whether an image is a dog or a cat would be a classification problem.    Some Common Classification Machine Learning Algorithms/models : - K Nearest Neighbors - Support Vector Machine - Logistic Regression - Random Forest Classifier - Decision Trees - Neural Networks Regression: On the other hand, regression algorithms attempt to predict a continuous quantity. An example of a regression problem would be when you're trying to predict the prices of homes, where the prediction could be an integer, fraction, or decimal number.  Some Common Regression Machine Learning Algorithms/models: - Linear Regressor - Polynomial Regressor - Lasso Regressor - Ridge Regressor - Elastic Net Regressor - Support Vector Regressor - Regression Trees - Neural Networks ...