EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Machine Learning Models

Home » Data Science » Data Science Tutorials » Machine Learning Tutorial » Machine Learning Models

Machine Learning Models

Introduction to Machine Learning Models

A machine learning model is the output of the training process and is defined as the mathematical representation of the real-world process. The machine learning algorithms find the patterns in the training dataset which is used to approximate the target function and is responsible for the mapping of the inputs to the outputs from the available dataset. These machine learning methods depend upon the type of task and are classified as Classification models, Regression models, Clustering, Dimensionality Reductions, Principal Component Analysis, etc.

Types of Machine Learning Models

Based on the type of tasks we can classify machine learning models in the following types:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

  • Classification Models
  • Regression Models
  • Clustering
  • Dimensionality Reduction
  • Deep Learning etc.

1) Classification

With respect to machine learning, classification is the task of predicting the type or class of an object within a finite number of options. The output variable for classification is always a categorical variable. For example, predicting an email is spam or not is a standard binary classification task. Now let’s note down some important models for classification problems.

  1. K-Nearest neighbors algorithm – simple but computationally exhaustive.
  2. Naive Bayes – Based on Bayes theorem.
  3. Logistic Regression – Linear model for binary classification.
  4. SVM – can be used for binary/multiclass classifications.
  5. Decision Tree – ‘If Else’ based classifier, more robust to outliers.
  6. Ensembles – Combination of multiple machine learning models clubbed together to get better results.

2) Regression

In the machine, learning regression is a set of problems where the output variable can take continuous values. For example, predicting the airline price can be considered as a standard regression task. Let’s note down some important regression models used in practice.

  1. Linear Regression – Simplest baseline model for regression task, works well only when data is linearly separable and very less or no multicollinearity is present.
  2. Lasso Regression – Linear regression with L2 regularization.
  3. Ridge Regression – Linear regression with L1 regularization.
  4. SVM regression
  5. Decision Tree Regression etc.

3) Clustering

In simple words, clustering is the task of grouping similar objects together. It helps to identify similar objects automatically without manual intervention. We can not build effective supervised machine learning models (models that need to be trained with manually curated or labeled data) without homogeneous data. Clustering helps us achieve this in a smarter way. Following are some of the widely used clustering models:

  1. K means – Simple but suffers from high variance.
  2. K means++ – Modified version of K means.
  3. K medoids.
  4. Agglomerative clustering – A hierarchical clustering model.
  5. DBSCAN – Density-based clustering algorithm etc.

4) Dimensionality Reduction

Dimensionality is the number of predictor variables used to predict the independent variable or target.often in the real world datasets the number of variables is too high. Too many variables also bring the curse of overfitting to the models. In practice among these large numbers of variables, not all variables contribute equally towards the goal and in a large number of cases, we can actually preserve variances with a lesser number of variables. Let’s list out some commonly used models for dimensionality reduction.

Popular Course in this category
Machine Learning Training (17 Courses, 27+ Projects)17 Online Courses | 27 Hands-on Projects | 159+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.7 (8,463 ratings)
Course Price

View Course

Related Courses
Deep Learning Training (15 Courses, 24+ Projects)Artificial Intelligence Training (3 Courses, 2 Project)
  1. PCA – It creates lesser numbers of new variables out of a large number of predictors. The new variables are independent of each other but less interpretable.
  2. TSNE – Provides lower dimensional embedding of higher-dimensional data points.
  3. SVD – Singular value decomposition is used to decompose the matrix into smaller parts in order to efficient calculation.

5) Deep Learning

Deep learning is a subset of machine learning which deals with neural networks. Based on the architecture of neural networks let’s list down important deep learning models:

  1. Multi-Layer perceptron
  2. Convolution Neural Networks
  3. Recurrent Neural Networks
  4. Boltzmann machine
  5. Autoencoders etc.

Which Model is the Best?

Above we took ideas about lots of machine learning models. Now an obvious question comes to our mind ‘Which is the best model among them?’ It depends on the problem at hand and other associated attributes like outliers, the volume of available data, quality of data, feature engineering, etc. In practice, it is always preferable to start with the simplest model applicable to the problem and increase the complexity gradually by proper parameter tuning and cross-validation. There is a proverb in the world of data science – ‘Cross-validation is more trustworthy than domain knowledge’.

How to Build a Model?

Let’s see how to build a simple logistic regression model using the Scikit Learn library of python. For simplicity, we are assuming the problem is a standard classification model and ‘train.csv’ is the train and ‘test.csv’ is the train and test data respectively.

Scikit Learn Library

Conclusion

In this article, we discussed the important machine learning models used for practical purposes and how to build a simple model in python. Choosing a proper model for a particular use case is very important to obtain the proper result of a machine learning task. To compare the performance between various models, evaluation metrics or KPIs are defined for particular business problems and the best model is chosen for production after applying the statistical performance checking.

Recommended Articles

This is a guide to Machine Learning Models. Here we discuss the basic concept with Top 5 Types of Machine Learning Models and how to built it in detail. You can also go through our other suggested articles to learn more –

  1. Machine Learning Methods
  2. Types of Machine Learning
  3. Machine Learning Algorithms
  4. What is Machine Learning?
  5. Hyperparameter Machine Learning

Machine Learning Training (17 Courses, 27+ Projects)

17 Online Courses

27 Hands-on Projects

159+ Hours

Verifiable Certificate of Completion

Lifetime Access

Learn More

0 Shares
Share
Tweet
Share
Primary Sidebar
Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Linear Regression Modeling
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Linear Regression Analysis
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Pytorch
    • PyTorch Versions
    • Single Layer Perceptron
    • PyTorch vs Keras
    • torch.nn Module
  • UiPath
    • What is UiPath
    • UiPath Careers
    • UiPath Architecture
    • UiPath Orchestrator
    • Uipath Reframework
    • UiPath Studio
  • Interview Questions
    • Machine Learning Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2020 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you
Book Your One Instructor : One Learner Free Class

Let’s Get Started

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

Special Offer - Machine Learning Training (17 Courses, 27+ Projects) Learn More