Common machine learning algorithms for beginners

Introduction

Machine learning is one of the hottest trendings in tech industries today.  Machine learning is very powerful to make prediction or recommendations based on huge amount of data sets. Machine learning has vast use cases for different domains either in the e-commerce business, finance, intelligence, sci-fi movies, marketing, real estate etc.

Amazon, Flipkart or other e-commerce giants recommend books or other products based on your previous purchased. Netflix suggests you new movies based on your past watchlist.

 

pic credit: Google images

Through this article, my intention is to present the most commonly used machine learning algorithm. This article basically designed for the data science and machine learning enthusiast. I am not going into details on what happens inside each algorithm but will cover a general overview of each algorithm. I will also provide each of algorithm’s python code in Scikit learn.

Key points in Machine learning algorithm

  • Independent variables: Independent variables are the variable on the basis them we predict our dependent variable. Independent variables are also called input variable, explanatory variable, features, predictors.
  • Dependent variable: The variable which outcomes you want to predict basically dependent variable depends upon independent variable. The dependent variable also called Target variable, Response variable, Output variable, Outcomes, Labels.
  • Training Set: The data on which perform data preprocessing, finding the pattern, made model, use different machine learning algorithms. We can say that training set is a collection of examples or data that comprises supervised experience.
  • Test Set: Test set is similar to training set that is used to evaluate the performance of model using some performance matrices.
  • Model: A model is something that is created by the training process. These are basically our machine learning algorithms, example are Decision tree model, Random forest. These algorithms are used for prediction.

There are three types of machine learning algorithm:

Supervised Learning: Supervised learning basically uses labeled data means it consists target/outcomes along with explanatory or predictor variables. Target variable is also known as the dependent variable and explanatory variable is known as the independent variable. Here we generate a function that maps input variables to the desired output.  Example of supervised machine learning is the Linear regression, Logistics regression, Decision tree, Support vector machines etc

How supervised machine learning works

Supervised learning is also of two types which are given below:
  • Regression: To predict the outcome of input variables where the output variable in the form of real-valued or continuous in nature. Example upcoming next two year sales of a company, height of a person, the price of a house in coming years etc. Linear regression falls into this category.
  • Classification: To predict the outcome of the input variables where the output variable is in form of categories. Example bank loan status yes or no, male or female, true or false etc. Logistics regression falls into this category.

Unsupervised Learning: Unsupervised machine learning algorithm deals with no any labeled data or target data. These types of algorithm group the huge amount of data or information into different subgroups. This is widely used for clustering or segmenting objects( customer, populations etc) into different groups. Example of unsupervised machine learning is K-means, PCA, Apriori etc.

Unsupervised learning is also of two types which are given below:
  • Clustering: Clustering is the procedure by which datasets are grouping into different group object, The object from the same group share the certain degree of similarity and very dissimilar to other groups of object. Example a company wants to find the group of the customer with similar behavior for the upcoming products. K-means is the example of the clustering algorithm.
  • Dimensionality Reduction: Dimensionality reduction means reducing the number of variables while maintaining it’s important features. Dimensionality reduction can also be done through feature extraction. Feature extraction processes by which higher dimensional data can reduce into lower dimensional space. PCA is an example of Dimensionality reductions.

Reinforcement Learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

List of important machine learning algorithms

  1. Linear Regression
  2. Logistic Regression
  3. Naive Bayes
  4. KNN
  5. SVM
  6. Decision Tree
  7. Random Forest
  8. Kmeans
  9. Dimensionality Reduction
  10. Gradient Boosting & ADA Boosting

Linear Regression

Linear regression is used to model a relationship between independent variables and the dependent variable. The equation of linear regression with one independent or explanatory variable is defined as

Y=W0 + W1X

Let suppose you go to a pizza place where you see the price of pizza according to their dimension. On the basis of that, you will also find the price of higher or desired dimension of pizza. Another example is a person height and weight combinations you can assume a person’s weights based on their height. There are lots of example of regression in our real life which we can observe visually and calculated with some parameter with the help of our mind.

 

Here the equation Y=W0 + W1X model a relationship based on best fit line which is also known as the regression line, and the vertical lines from regression line to the sample points are called residuals or offsets.

In this equation

Y= Target variable or Dependent variable.

W1=  Slope or coefficient of the input variables.

X= Input variable or Independent variable.

W0= Intercept of the y-axis.

In this equations, we find the value of a and b so that it can minimize the sum of the square distance between regression line or best fit line form data points.

Linear regression has two types:

  • Simple Linear Regression: The relation based on the single input variable.
  • Multiple Linear Regression: The relationship based on multiple input variables or it is a generalized simple model.

Code with Scikit learn(Python)

# Import linear regression  from sklearn 
from sklearn.linear_model import LinearRegression
# create LinearRegression object
model = LinearRegression()
#fit into model on training data
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

Logistic Regression

Logistics regression is a type of classification algorithm unlikely to linear regression it predicts the discrete value example either bank will give the loan or not, it will be rain tomorrow or not, a student will pass or not etc. Basically Logistic regression best suited for Binary classification but it can be extended as multiclass classification through OvR technique.

We can understand this through a simple example.

let suppose you are a banker and you want to know that among the 1000 people who are eligible for the bank loan or not here you defined the probability range between 0 to 1. The outcomes or predicted value will come on the basis of different scenarios like person’s past credit score, annual income, accommodation area etc.

It is called logistics regression due to it uses logistics function which predicts the probability that certain samples belong to the particular class. Sometimes it also called Sigmoid function due to it’s S character shape.

     Logistic function   p =1/1+e^-y  where y=b0+b1x1+b2x2+.......+bmxm = b^tx

 

Code with Scikit learn(Python)

# Import linear regression  from sklearn 
from sklearn.linear_model import LogisticRegression
# create LogisticRegression object
model = LogisticRegression()
#fit into model on training data
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

 Naive Bayes

Naive Bayes algorithm is basically based on Bayes’ Theorem. Naive Bayes algorithm comprises two types of probability (1) Probability of each class separately or independents to each other. (11)conditional probability is given each class given each separate class.  Let’s take an example of Flower if we want to predict flower type by its petal length and width since these features are independent of each other.

Naive Bayes is naive because it assumes all features are independent of each other but in real life is happens very in few cases or impossible. But it is very useful for complex problems.

Code with Scikit learn(Python)

# Import GaussianNB from sklearn 
from sklearn.naive_bayes import GaussianNB
# create GaussianNB object
model = GaussianNB()
#fit into model on training data
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

 KNN(K-Nearest Neighbors)

The KNN is the simplest machine learning algorithm, To make the prediction of new data point the algorithm finds the closest data point in the training datasets. KNN uses for both cases either classification or regression.

How KNN works it can be understood by these steps:

  1. Choose the number of K and a distance function(metric), The most frequently used distance function is the Euclidean distance for continuous variables and Hamming distance for categorical variables.
  2. Find the k-nearest neighbor of the sample, we want to classify.
  3. Assign class label by the majority of the vote.

In this figure, the new data point(?) assigned to triangle class label based on majority vote among its five instances.

Code with Scikit learn(Python)

from sklearn.neighbors import KNeighborsClassifier
# create KNeighborsClassifier object 
model = KNeighborsClassifier(n_neighbors=5)
#fit into model on training data 
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

SVM(Support Vector Machine)

Support vector machine is the perhaps most popular machine learning algorithm. Support vector machine mostly used for classifications problem but we can also use it in regression problems.

Support vector machine classifies the n-dimensional data space by hyperplane, with an intuition of maximum margin. The margin defined as the separating hyperplanes and the data points that are very close to the hyperplane called support vectors.

Support vector machine works both for linear and nonlinear problems.

Code with Scikit learn(Python)

from sklearn import svm 
# create svm object 
model = svm.svc()
#fit into model on training data 
model.fit(X,y) 
#predict the model on test data
model.predict(x_test)

Decision Tree

Decision Tree is widely used machine learning algorithm both for classification and regression. Basically, Decision tree learns a hierarchy of “if-else” questions leading to a decision.  Decision tree breaks our training datasets based on asking some series of questions.

In this example, leaves represent classes to play and don’t play of cricket game based on weather conditions. We can also break our decision on real-valued data sets for example if we would like to know the people who have more than $100 salary then we can simply ask a binary question “salary>$100”.

Code with Scikit learn(Python)

from sklearn.tree import DecisionTreeClassifier
# create DecisionTreeClassifier object
model = DecisionTreeClassifier(criterion='gini')
#fit into model on training data
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

Random Forest

Random forest is a type ensemble learning. The objective of ensemble learning is to combine weak learner to build more robust and strong learner. So we can say that Random forest is the collections of trees. A Random forest has the better generalization and less suspect to overfitting, But the things we have to care about choosing the number of trees typically the larger the number of trees, we decrease the randomness thus forest is more likely to overfit. We can also access important features by random forests.

Code with Scikit learn(Python)

from sklearn.ensemble import RandomForestClassifier
# create RandomForestClassifier object
model = RandomForestClassifier()
#fit into model on training data
model.fit(X,y)
#predict the model on test data
model.predict(x_test)

K-Means

K-means is an unsupervised machine learning algorithm basically used for clustering problems. K-means is a type of prototype-based clustering. In prototype-based clustering, each cluster represented by a prototype as centroid for continuous data types.

This algorithm works by assigning each data points to the closest cluster and then setting each cluster as the mean of the data points that are assigned to it.

In this figure, we are looking for three clusters so we declare three data points as cluster center, Now the iterative process starts, In fig Assign points(1): each data points are assigned to the cluster center it is closest. In fig Recompute Center(1): cluster centers are updated to mean the point and these process repeat until cluster center remains unchanged.

Code with Scikit learn(Python)

import sklean.cluster import KMeans
# create object of Kmeans
# in kmeans we have to define k early
model=KMean(n_cluster=3, random_state=0)
model.fit(X)
model.predict(x_test)

Dimensionality Reduction

Sometimes while working on the huge amount of dataset we need to identify the pattern and features. Dimensionality reduction is a data compression techniques with the goal of maintaining most of the relevant information. It is typically used to improve computational efficiency but can also help to reduce the curse of dimensionality.

Two commonly used dimensionality reduction technique is:

  • Principal Component Analysis: PCA is unsupervised linear transformation techniques. PCA helps us to identify the pattern in data based on correlations between features or we can say that PCA finds the dimension of maximum variance into large datasets and project it into new subspace with a fewer dimension than the original one.
  • Linear Discriminant Analysis: LDA finds the feature subspace that optimizes class separately. This is a supervised method.LDA is better dimensional reduction technique than PCA.

Code with Scikit learn(Python)

from sklearn.decomposition import PCA
# create object of PCA
model=PCA(n_components=2)
# PCA on training data 
model.fit_transform(X)
#Mapping the features
model.transform(X)
model.transform(x_test)

# LDA code

from sklearn.ldaimport LDA
# create object of LDA
model=LDA(n_components=2)
# PCA on training data
model.fit(X,y)
# transform test data
model.transform(x_test)

Boosting & ADA Boosting

Gradient Boosting and ADA Boosting is an ensemble technique that combines weak classifier to make a strong one. These algorithms basically make the prediction of higher accuracy.

AdaBoosting was first developed Boosting algorithm for Binary classifications. AdaBoost is used with Decision trees. It monitoring each performance of tree in each training instances and how much attention the next tree will be required.

This approach boosts the accuracy of any predictors and is widely used in practice, It is used by most of the teams who win the Kaggle machine learning competitions.

Code with Scikit learn(Python)

from sklearn.ensemble import GradientBoostingClassifier
# create object
model=GradientBoostingClassifier(n_estimators=100,learning_rate=1.0,max_depth=1)
#train model
model.fit(X,y)
model.predict(x_test)

Last Note

In this article, we saw commonly used machine learning algorithms. Before starting this article my motive was to present a basic idea about machine learning algorithm by which people who are keenly interested to get into machine learning and data science can start right away with python codes and the basic idea of these algorithms. I also recommend you to practice the problems and try to understand the mechanism of these algorithms.

Did you find this article helpful? Please share your thoughts in the comment section below.

About Mitra N Mishra 35 Articles
Mitra N Mishra is working as a full-stack data scientist.

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