Top Machine Learning Algorithms You Should Know

INTRODUCTION

The field of Machine Learning (ML) has seen growth in years as its algorithms have become essential components, in a wide range of industry applications. These algorithms are designed for tasks and are typically grouped into unsupervised learning categories; they also include more advanced methods such, as reinforcement learning and ensemble techniques.

Let’s dive deeper into few of these algorithm types to understand them better.

Linear regression

Linear regression is a method used to analyze the relationship, between two variables.
Application scenario examples include forecasting events, like real estate values prediction models and anticipated stock market trends.
Linear Regression works by establishing a connection, between a variable and several independent variables through the creation of a line that best fits the data points accurately reducing the margin of error between predicted and actual values which makes it an effective tool, for regression purposes.
Strengths and Weaknesses; Linear Regression is simple to understand. Doesn’t require a lot of power to run calculations efficiently. However it works best when there is a straight line relationship, between the variables and may struggle with data that has relationships that are not linear, in nature.

Logistic Regression

It is widely used in fields such as spam detection, loan default prediction and disease diagnosis.
Logistic Regression, Despite the term regression in its name you can not misunderstand that it for regression this is a classification model It uses the sigmoid function to map predictions to probabilities hence makes it ideal for binary outcomes.
Logistic Regression is Simplest form of Classification (Linearity in Data), efficient for linear separable data. But it also suffers from complexity (non-linearity).

Decision Trees

Credit scoring, fraud detection, customer segmentation; Classification and Regression applications
A decision tree splits the dataset into branches using feature values, and we end up with a model that has this tree-like structure where each branch would be something like a possible decision. The model will choose splits that maximizes information gain or minimises impurity.
Decision Trees can be incredibly easy to elucidate, are good with non-linear relationships and work very well out of the box in handling numeric as categorical data. The only drawback is that trees are prone to over fitting (especially with deep trees).

Support Vector Machines (SVM)

Application: Image recognition, text categorization and bioinformatics
SVM tries to find the hyperplane that distinctly classifies many different classes with maximum margin. By applying kernel functions, the algorithm can map non-linear data to a higher dimensional space which makes it able to deal with complex distributions.
Advantages and Disadvantages: SVM is more effective in high dimensional space, it works well with clear margin separations. But, They can be computationally expensive and hyper-parameters of the model need to carefully tuned specially when using with large datasets.

K-Nearest Neighbors (KNN)

Applications: Classification problems (recommendation systems, image recognition etc.)
How KNN works: (Instance-based): It predicts the class label of a new data point based on most frequent class among its k-nearest neighbors. It is good for data with complex non-linear decision boundaries
It is very easy to understand, simple to implement but less efficient for large datasets with uneven boundaries. On the other hand, this operation may be computationally expensive with big datasets and it can suffer from feature scaling.

Naive Bayes

Application: Text Classification, Spam Filters, Sentiment Analysis and Document Categorization.
Naive Bayes is an implementation of the classes based on Bayesian theorem and its easy to build but nevertheless it considers that every one of the Predictors \(P_x\) are independent from each other. It is the probability of occurrence of classes for a given set of input features it’s a probabilistic algorithm.
It is computationally efficient, suitable for very large datasets, and great at tasks with lots of text. But the assumption of independence may not be so good for features that are highly correlated.

Random Forest

Application : Classication and Regression (Customer churn, Loan approval, Share price)
Random Forest is a set of decision trees that are combined to improve the accuracy and overcome overfitting. The model consists of number of trees and each tree trained on the random subset from the original data set then final prediction based on all decision taken by different trees.
Random Forests are strong, works well with bigger datasets as opposed to a single decision tree making predictions through the forest. But they can also be very computationally intensive and often offer no interpretability.

Gradient Boosting (XGBoost, LightGBM)

Applications: Common in predictive modeling competitions, financial tasks.
Gradient Boosting is a strong model that constructs models in order, where each new model tries to correct the mistake made by previous ones. XGBoost and LightGBM are both algorithms that do this to great effect, making them extremely accurate.
Gradient Boosting works best with longer data patterns, but it can be computationally expensive without a good regularization which then leads to over fitting.