## Similar Posts

### Handling Missing Data

Missing data is basically the values that are missing in our dataset, and that would be meaningful for our machine learning project if observed. In this article, we’ll see how missing data can be anything from missing sequence, incomplete feature, files missing, information incomplete, data entry error, etc. Most datasets in the real world contain…

### Accuracy, Specificity, Precision, Recall, and F1 Score for Model Selection

You must have heard about the accuracy, specificity, precision, recall, and F score since they are used extensively to evaluate a machine learning model. You must have come across 2 specific types of errors called “type 1” and “type 2” errors. In this post, we will cover all these matrices one by one. To understand…

### 3. SVM – Support Vector Machine

Support Vector Machines are a set of supervised learning methods used for classification, regression, and outlier detection. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyper-plane. The idea of SVM is simple: It takes the past data as an input and outputs a line or a hyper-plane which separates…

### 1) Reduce Overfitting: Using Regularization

This is Part 1 of our article. In regression analysis, the features are estimated using coefficients while modeling. Also, if the estimates can be restricted, shrunk, or regularized towards zero, then the impact of insignificant features might be reduced and would prevent models from high variance with a stable fit. Regularization shrinks the coefficient estimates towards zero….

### Steps of Machine learning

Different stages in machine learning model: Please refer to our section on machine learning models. 1. Gathering Data First thing first. You need to understand the Business Problem you are facing. You have to consider what the main goals of your problem are. Data is power. When the problem is clear, and an appropriate machine…

### Kernel Functions for SVM

Kernel functions or Kernel trick can also be regarded as the tuning parameters in an SVM model. They are responsible for removing the computational requirement to achieve the higher dimensional vector space and deal with the non-linear separable data. Here’s our post on the SVM model. The SVM kernel could be a function that takes low…