Question

Can you please briefly describe when to use each algorithm?

Supervised algorithms (Machine learning):

- k-Nearest Neighbours

- Support Vector Machines (SVM)

Unsupervised algorithms (Machine learning) :

- K-means clustering

- Cross-Validation

Answer 1

SUPERVISED ALGORITHM

-. K-NEAREST NEIGHBORS

KNN can be used for both classification and regression predictive problems.However,it is more widely used in classification problems in the industry.To evaluate any technique we generally look at three important aspects.

1. Ease to implement output

2. Calculation time

3. Predictive power

It is commonly used for its easy of interpretation and low calculation time.

SUPPORT VECTOR MACHINES (SVM)-

Support vector machine is a supervised machine learning algorithm which can be used for both classification or regression challenges.it is mostly used in classification problems.In this algorithm we plot each data item as a point in n-dimensionàl space with the value of each feature being the value. a particular coordinate.In general SVM are very good when you have a huge number of features.

SVM means that the algorithm calculate does not have to be a straight line.the benefit is that you can capture much more complex relationships between your data points without having to perform difficult transformation on your own.

UNSUPERVISED ALGORITHMS

K-MEANS CLUSTERING:-

K-means clustering is a type of unsupervised. ,which is used when you have unlabeled data(I.e. data without defined categories).The goal of this algorithm is to find groups in the data,with the number of groups represented by the variable K .The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided.

This can be used to confirm business assumptions about what type of groups exist or to identify unknown groups in complex data sets.

CROSS VALIDATION:-

Cross validation is a statistical method to estimate the skills of machine learning model.It is commonly used in applied Machine to compare and select a model for a given predictive modeling problem because it is easy to understand,easy to implement,a nd results in skill estimates that generally have a lower bias than other methods.

There are commonly used variations on cross validation SUVs as satisfied and repeated that are available. Sckit-learn