Neural Networks are universal approximators that maps data to information. What does this mean? Can Neural Networks solve any problem? Neural Networks are a proven solution for scene-by-scene/frame-by-frame analysis, stock price prediction, in retail, and for many other purposes. Many of us are using it at the enterprise level, but how many of us truly understand it?
To answer the question, ‘Can Neural Networks solve any problem?’, let’s take it from the basics. A NeuralNet is made up of vertically stacked components called layers: input, hidden, and output. Each layer consist of a certain number of neurons. The input layer…
As the variables in the dataset increases, its dimension increases which can have the following challenges:
Hence, Dimensionality Reduction is the process of reducing the dimensions of the data to ensure it conveys maximum information. There are two major ways to reduce the dimensions of a dataset: 1. By selecting only useful features from data based on different…
In the second part of the article, Evaluation Metrics, we will discuss different metrics to evaluate regression algorithms. (First part can be found here.)
In regression, we calculate error by comparing predicted values with actual values. Error determines how far predicted values are from the actual value. The sign (+ or -) of the error lets us know the direction in which error varies from the best-fit regression line.
| Actual Value | Predicted Value | Error…
This is part 1 of the 2 article series where we discuss different evaluation metrics for Machine Learning (ML) problems. Evaluating an algorithm’s output is as important as modeling the algorithm itself. Evaluating a program helps in determining how impactful is the program and how it could be improved. In this article, we will be reviewing evaluation metrics for classification. So, let’s begin.
Confusion Matrix is an N x N matrix, where N represents the number of categories in the target variable (For example, 1 and 0 are two classes/categories in the survived column of the titanic dataset).
Continuous improvement is better than delayed perfection. -MT