Different kernels in SVM

 There are four types of kernels in SVM.

Comments

Post a Comment

Popular posts from this blog

Decision Tree algorithm

Image
A decision tree is a supervised machine learning algorithm mainly used for Regression and Classification. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision tree can handle both categorical and numerical data.  
Read more

Univariate, bivariate and multivariate analysis

  Univariate  analyses  are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis. The  bivariate  analysis  attempts to understand the difference between two variables at a time as in a scatterplot.  For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. Multivariate analysis  deals with the study of more than two variables to understand the effect of variables on the responses.
Read more

Bias-variance trade-off

Image
  Bias: Bias is an error introduced in your model due to oversimplification of the machine learning algorithm. It can lead to underfitting. When you train your model at that time model makes simplified assumptions to make the target function easier to understand. Low bias machine learning algorithms — Decision Trees, k-NN and SVM High bias machine learning algorithms — Linear Regression, Logistic Regression Variance: Variance is error introduced in your model due to complex machine learning algorithm, your model learns noise also from the training data set and performs badly on test data set. It can lead to high sensitivity and overfitting. Normally, as you increase the complexity of your model, you will see a reduction in error due to lower bias in the model. However, this only happens until a particular point. As you continue to make your model more complex, you end up over-fitting your model and hence your model will start suffering from high variance.   B...
Read more