Brief overview to Machine learning Algorithms
In this modern world technical revolution has taken place thus artificial intelligence and machine learning has come into existence because of its accurate predictions. However day by day it will gain more and more attention in industries. Thus first of all as an engineers we should be well known to these advanced techniques of machine learning and its algorithms.
We Should be aware of what is Machine Learning?
Machine learning is a field of Artificial Intelligence, which is allowed to software applications for making accurate results. Algorithms are built through which input is received and after statistical analysis output value is predicted. Because the algorithms are trained from dataset and thus learn from data, finally improved results are predicted. Furthermore, improved functionality of computers.
Machine learning algorithms can be supervised, unsupervised and reinforcement learning.
In supervised learning algorithms training of model is done using some previous dataset. It require humans for providing input and output. With some previous dataset training of model is done. After completion of training, algorithm applies the learnt things for prediction of new data.
For more complex problems unsupervised learning is used. Interferences of dataset from input data is drawn. It is done for unlabeled data so cluster analysis method is used i.e from data it finds patterns from unclassified data.
This type of learning is based on self learning process. Machine learn its behaviour by using feedback from the environment to maximize its performance.
10 Algorithms of Machine Learning
1. Linear Regression: For statistical technique linear regression is used in which value of dependent variable is predicted through independent variables. A relationship is formed by mapping the dependent and independent variable on a line and that line is called regression line which is represented by Y= a*X + b.
Where Y= Dependent variable (e.g weight).
X= Independent Variable (e.g height)
b= Intercept and a = slope.
2. Logistic Regression: In logistic regression we have lot of data whose classification is done by building an equation. This method is used to find the discrete dependent variable from the set of independent variables. Its goal is to find the best fit set of parameters. In this classifier, each feature is multiplied by a weight and then all are added. Then the result is passed to sigmoid function which produces the binary output. Logistic regression generates the coefficients to predict a logit transformation of the probability.
3. Decision Tree: It belongs to supervised learning algorithm. Decision tree can be used to classification and regression both having a tree like structure. In a decision tree building algorithm first the best attribute of dataset is placed at the root, then training dataset is split into subsets. Splitting of data depends on the features of datasets. This process is done until the whole data is classified and we find leaf node at each branch. Information gain can be calculated to find which feature is giving us the highest information gain. Decision trees are built for making a training model which can be used to predict class or the value of target variable.
4. Support vector machine: Support vector machine is a binary classifier. Raw data is drawn on the n- dimensional plane. In this a separating hyperplane is drawn to differentiate the datasets. The line drawn from centre of the line separating the two closest data-points of different categories is taken as an optimal hyperplane. This optimised separating hyperplane maximizes the margin of training data. Through this hyperplane, new data can be categorised.
5. Naive-Bayes: It is a technique for constructing classifiers which is based on Bayes theorem used even for highly sophisticated classification methods. It learns the probability of an object with certain features belonging to a particular group or class. In short, it is a probabilistic classifier. In this method occurrence of each feature is independent of occurrence another feature. It only needs small amount of training data for classification, and all terms can be precomputed thus classifying becomes easy, quick and efficient.
6. KNN: This method is used for both classification and regression. It is among the simplest method of machine learning algorithms. It stores the cases and for new data it checks the majority of the k neighbours with which it resembles the most. KNN makes predictions using the training dataset directly.
7. K-means Clustering: It is an unsupervised learning algorithm used to overcome the limitation of clustering. To group the datasets into clusters initial partition is done using Euclidean distance. Assume if we have k clusters, for each cluster a centre is defined. These centres should be far from each other, and then each point is examined thus added to the belonging nearest cluster in terms of Euclidean distance to nearest mean, until no point remains pending. A mean vector is re-calculated for each new entry. The iterative relocation is done until proper clustering is done. Thus for minimizing the objective squared error function process is repeated by generating a loop.
Final results of the K-means clustering algorithm are:
1. The centroids of the K clusters, which are used to label new entered data.
2. Labels for the training data .
8. Random Forest: It is a supervised classification algorithm. Multiple number of decision trees taken together forms a random forest algorithm i.e the collection of many classification tree. It can be used for classification as well as regression. Each decision tree includes some rule based system. For the given training dataset with targets and features, the decision tree algorithm will have set of rules. In random forest unlike decision trees there is no need to calculate information gain to find root node. It use the rules of each randomly created decision tree to predict the outcome and stores the predicted outcome. Further it calculates the vote for each predicted target. Thus high voted prediction is considered as the final prediction from the random forest algorithm.
9. Dimensionality Reduction Algorithms: It is used to reduce the number of random variables by obtaining some principal variables. Feature extraction and feature selection are types of dimensionality reduction method. It can be done by PCA, Principal component analysis is a method of extracting important variables from large set of variables. It extracts the low dimensionality set of features from high dimensional data. It is used basically when we have more than 3 dimensional data.
10. Gradient boosting and Ada Boost Algorithms : Gradient boosting algorithm is a regression and classification algorithm. AdaBoost only selects those features which improves predictive power of the model. It works by choosing a base algorithm like decision trees and iteratively improving it by accounting for the incorrectly classified examples in the training set. Both of algorithms are used for the boosting of the accuracy of predictive model.