Credit: Google News
Machine Learning: Up and Coming Modern Day Career Pick
Harvard Business Review is calling machine learning one of the “Sexiest Jobs of the 21st Century.” There is no lack of opportunities in the data science field, which is ever-expanding as technology advances.
If you are in the beginning stages of your machine learning journey, then look no further. We’ve got the basics covered down below with our top 5 algorithm picks.
In the world of machine learning and statistics, linear regression is one of the most popular algorithms. This algorithm is a statistical model. The basis is that you have to analyse two factors to predict the relationship between dependent and independent variables in linear regression.
The main factors of linear regression:
1) The significant predictors of the outcome variables.
2) How noteworthy the regression line is for making predictions with the highest possible accuracy.
The formula of linear regression:
Using linear regression, you can represent the relationship between an independent variable x and a scalar dependent variable y. The equation that represents linear regression can be written as y=wx+b where w is a vector of weight parameters, b stands for the value of output for zero input and x,y,w all are vectors of real numbers.
A few real-life applications of linear regression are as follows:
- Is used to predict the GDP of a country.
- Can be used to predict the numbers of runs made by players in sports matches.
- The economic growth of a state or country in the coming quarter can be determined by it.
- The future price of a product can be predicted using linear regression.
The name logistic regression comes from the main function of this method, which is logistic function. Machine learning got this technique from the field of statistics and it is known as the go-to method for binary classification problems. The shape of the logistic function is like a big S and it can take any real-valued number to plot into the range 0 to 1. The logistic function is also addressed as the sigmoid function.
The equation for logistic regression is y= e^(b0 + b1*x) / (1 + e^(b0 + b1*x)). Here, b0 is the bias, b1 is the coefficient for the single input value that is x and y is the predicted output.
Logistic regression is largely used in the business world. Some real-life applications of logistic regression are:
- For HR representatives of companies who want to predict the pattern of days taken off work by his/her employees depending upon their individual characteristics, logistic regression can help in this.
- With logistic regression, a marketing consultant can predict if the subsidiary of his organization will face loss, profit, or break simply by analyzing the characteristics of the subsidiary operations.
- A banking sector can predict if his customers will not repay a loan based on their previous transaction patterns.
Linear Discriminant Analysis
To overcome the limitations of logistic regression, machine learning utilizes Linear Discriminant Analytics, or LDA. Logistic regression algorithm works fine with two-class classification problems, but if you have to deal with more than two classes, then you have to use LDA algorithm as the linear classification technique. LDA comprises statistical properties of your data which have been calculated for each class. If you choose a single input variable, it will include:
- The mean value calculated for each class
- The difference calculated across all classes
LDA is basically used as a dimensional reduction technique as a pre-processing step for machine learning and pattern-classification applications. It reduces computational costs as well.
This algorithm is a simple yet powerful method to solve classification predictive modelling problems and it accepts data with a Gaussian distribution or bell curve.
Classification and Regression Trees
In the leading scenario of predictive modelling machine learning, decision trees are one of the most important algorithms to follow. Representation-wise, you can relate the decision tree model to a binary tree. Like you have read in data structures and algorithms, each node denotes a single input variable(x) and a split point on that x.
It assumes that the variable is numeric. The output variable (y) is denoted by the leaf nodes of the tree and used to make the desired prediction. By using classification and regression trees, it is possible to make predictions very fast. They don’t ask for any special preparation for your data and produce accurate results often for a large range of problems.
A powerful algorithm for predictive modelling is Naive Bayes. This simple yet effective model consists of two types of probabilities that you can calculate from your training data. They are the conditional probability for each class with x value in each and the probability of each class.
After you are done with the calculation part, you can use Bayes Theorem into the probability model to make predictions for new data. If your data holds a real value, it can be assumed as a Gaussian distribution, or bell curve, and you can evaluate these probabilities easily. In this scenario, each input variable is assumed to be independent and Naïve Bayes obtained the term naïve for this reason. To solve complex problems in a huge range, the technique is very effective.
Credit: Google News