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Machine Learning Algorithms Guide

This document discusses various machine learning algorithms for regression, classification, and clustering problems. It begins by explaining linear regression and logistic regression models. It then lists decision trees, random forests, XGBoost, KNN, naive Bayes, and SVM as examples of regression and classification algorithms. For clustering, it mentions k-means clustering. The document goes on to categorize different machine learning algorithms into groups like regression algorithms, instance-based algorithms, regularization algorithms, decision tree algorithms, and more. It provides examples of algorithms within each category.

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suresh reddy
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127 views3 pages

Machine Learning Algorithms Guide

This document discusses various machine learning algorithms for regression, classification, and clustering problems. It begins by explaining linear regression and logistic regression models. It then lists decision trees, random forests, XGBoost, KNN, naive Bayes, and SVM as examples of regression and classification algorithms. For clustering, it mentions k-means clustering. The document goes on to categorize different machine learning algorithms into groups like regression algorithms, instance-based algorithms, regularization algorithms, decision tree algorithms, and more. It provides examples of algorithms within each category.

Uploaded by

suresh reddy
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Linear Models - Linear Regression (Continuous Values)

dataset(Experience, Salary) -> Problem statement -> Predict


the Salary based on Experince?
X - Experience ,
Y - Salary
Basic => y=mx+c -> salary = slope * experience + intercept

Logistic Regression (1,0)


dataset (Amount_smoking, Cancer)
Cancer =1,0

Machine Learning (Reg and Classifier)


Decision Tree
ensemble - Bagging (Random Forest)
Boosting (XGBoost)

KNN,NB,SVM

Clustering :
To group the segments (like customers)
based on the spending -> Premium, Gold , Silver

KMeans

Text Mining
NB...

Problem -> Amazon -> Classify the User comment

Links - StatQuest - for conceptual

Types of Problem:
Regression - Linear Reg, DT, RF, SVM
Classification - Logistic

Clustering - Kmeans, Hierarchial clustering,


-----------------------------------------------------------------------------------
-------------------------

ensemble midel :
collection of individual model and aggreate the result for final
result.
2 types of ensemble
1.homogenious -->same techinque for of models desecion
tree and tree ensemble models
2.heterogenious ---> differrent techinque of all models

again 2 types
1.manual
2.Automated
but here considered only automated and homogenious.

baggingf--> boost strap aggrigation


tuning parameters :
DT tunnning parameters+no.ofmodels

ensembling prefered only randamforest and XGboost

1. Regression Algorithms
Ordinary Least Squares Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines (MARS)
Locally Estimated Scatterplot Smoothing (LOESS)
2. Instance-based Algorithms
k-Nearest Neighbour (kNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
Locally Weighted Learning (LWL)
3. Regularization Algorithms
Ridge Regression
Least Absolute Shrinkage and Selection Operator (LASSO)
Elastic Net
Least-Angle Regression (LARS)
4. Decision Tree Algorithms
Classification and Regression Tree (CART)
Iterative Dichotomiser 3 (ID3)
C4.5 and C5.0 (different versions of a powerful approach)
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
M5
Conditional Decision Trees
5. Bayesian Algorithms
Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
Bayesian Network (BN)
6. Clustering Algorithms
k-Means
k-Medians
Expectation Maximisation (EM)
Hierarchical Clustering
7. Association Rule Learning Algorithms
Apriori algorithm
Eclat algorithm
8. Artificial Neural Network Algorithms
Perceptron
Back-Propagation
Hopfield Network
Radial Basis Function Network (RBFN)
9. Deep Learning Algorithms
Deep Boltzmann Machine (DBM)
Deep Belief Networks (DBN)
Convolutional Neural Network (CNN)
Stacked Auto-Encoders
10. Dimensionality Reduction Algorithms
Principal Component Analysis (PCA)
Principal Component Regression (PCR)
Partial Least Squares Regression (PLSR)
Sammon Mapping
Multidimensional Scaling (MDS)
Projection Pursuit
Linear Discriminant Analysis (LDA)
Mixture Discriminant Analysis (MDA)
Quadratic Discriminant Analysis (QDA)
Flexible Discriminant Analysis (FDA)
11. Ensemble Algorithms
Boosting
Bootstrapped Aggregation (Bagging)
AdaBoost
Stacked Generalization (blending)
Gradient Boosting Machines (GBM)
Gradient Boosted Regression Trees (GBRT)
Random Forest
12. Other Algorithms
Computational intelligence (evolutionary algorithms, etc.)
Computer Vision (CV)
Natural Language Processing (NLP)
Recommender Systems
Reinforcement Learning
Graphical Models

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