Top 5 Most Used Machine Learning Algorithms in Python
Machine learning is the concept of programming the machine in such a way that it learns from its experiences and different examples, without being programmed explicitly. It is an application of AI that allows machines to learn on their own. Machine learning algorithms are a combination of math and logic that adjust themselves to perform more progressively once the input data varies. Being a general-purpose, easy to learn and understand language, Python can be used for a large variety of development tasks. It is capable of doing a number of machine learning tasks, which is why most algorithms are written in Python.
The process of creating machine learning algorithms is divided into 2 parts – Training and Testing Phase. Even though there is a large variety of machine learning algorithms, they are grouped into these categories: Supervised Learning, Unsupervised learning, and Reinforcement learning.
In this article, we’ll talk about 5 of the most used machine learning algorithms in Python from the first two categories.
It is one of the most popular Supervised Machine Learning algorithms in Python that maintains an observation of continuous features and based on it, predicts an outcome. It establishes a relationship between dependent and independent variables by fitting a best line. This best fit line is represented by a linear equation Y=a*X+b, commonly called the regression line.
The regression line is the line that fits best in the equation to supply a relationship between the dependent and independent variables. When it runs on a single variable or feature, we call it simple linear regression and when it runs on different variables, we call it multiple linear regression. This is often used to estimate the cost of houses, total sales or total number of calls based on continuous variables.
A decision tree is built by repeatedly asking questions to the partition data. The aim of the decision tree algorithm is to increase the predictiveness at each level of partitioning so that the model is always updated with information about the dataset.
Even though it is a Supervised Machine Learning algorithm, it is used mainly for classification rather than regression. In a nutshell, the model takes a particular instance, traverses the decision tree by comparing important features with a conditional statement. As it descends to the left child branch or right child branch of the tree, depending on the result, the features that are more important are closer to the root. The good part about this machine learning algorithm is that it works on both continuous dependent and categorical variables.
A supervised machine learning algorithm in Python that is used in estimating discrete values in binary, e.g: 0/1, yes/no, true/false. This is based on a set of independent variables. This algorithm is used to predict the probability of an event’s occurrence by fitting that data into a logistic curve or logistic function. This is why it is also called logistic regression.
Logistic regression, also called as Sigmoid function, takes in any real valued number and then maps it to a value that falls between 0 and 1.
