Machine Learning Algorithms

Machine Learning Algorithms

Recent studies reveal that machine learning algorithms have been expected to replace about 25% of the jobs easily across the world. With faster growth in the availability of programming tools like R and Python and big data, machine learning has acquired the mainstream presence for the data scientists. Being highly automated and self-modifying the Machine learning applications continuously show improvement with little human intervention as they tend to learn with more data. For example the recommendation algorithm of Netflix learns more and more about the likes and dislikes of the viewers depending upon the shows they watch. Some specialized machine learning algorithms have been developed in order to address the complex nature of several real-world data problems i.e. these provide a perfect solution to it.

For all those who have just started with the basics of machine learning and are struggling to understand the same, we have discussed some of the popular machine learning algorithms that the data scientists use. Machine learning algorithms are primarily classified into three different categories. These are:

Supervised Machine Learning Algorithms

The algorithms that make predictions on a specialized set of samples are termed as supervised machine learning algorithms. Within the value labels assigned to the data points the supervised machine learning algorithms search for patterns.

Unsupervised Machine Learning Algorithms

In this, there are no labels related to the data points. In order to make the complex data look simple and organized for the analysis, the machine learning algorithms organize them into a group of clusters to describe its structure.

Reinforcement Machine Learning Algorithms

The reinforcement Machine Learning Algorithms choose an action that depends upon every data point and then later realizes that how good was the decision. The algorithm keeps changing its strategy with time in order to learn better and achieve the best.

Common Machine Learning Algorithms

1) Naive Bayes Classifier Algorithm

It would be challenging and nearly impossible to manually classify a document, a web page, an email or any other lengthy text. This is where we need a Naïve Bayes Classifier machine learning algorithm. A classifier refers to the function that allocates the element value of a population from any of the available categories. For example- One of the popular applications of  Naïve Bayes algorithm is Spam Filtering. Thus Spam filter in this algorithm is a classifier responsible for assigning a label Spam or Not Spam to every email.

Grouped by similarities that work on the popular Bayes Theorem of Probability the Naive Bayes Classifier is one of the most popular learning methods. It is used for building up of machine learning models, especially for the document classification and disease prediction. It is an easy classification of the words for the subjective content analysis based upon the Bayes Probability Theorem.

When to use the Naive Bayes Classifier Machine Learning algorithm?

  1. When you have a large or moderate training data set.
  2. When the instances comprise of several attributes.
  3. When the classification parameter, attributes describing the instances are conditionally independent.

Applications of Naive Bayes Classifier

  1. Sentiment Analysis– This is used for analyzing the status updates at facebook that expresses positive or negative emotions.
  2. Document Categorization– Google makes use of the document categorization in order to index the documents and find its relevance scores i.e. the PageRank. PageRank mechanism takes into consideration the pages that have been marked as important in the databases that were resolved and classified through a document classification technique.
  3. We also use Naive Bayes Algorithm for the classification of news articles about Entertainment, Technology, Sports, Politics, etc.
  4. Email Spam Filtering– Google Mail makes use of Naive Bayes algorithm for the classification of your emails as Spam or Not Spam

Benefits of the Naive Bayes Classifier Machine Learning Algorithm

  1. When the input variables are categorical the Naive Bayes Classifier algorithm performs well. 
  2. When the Naive Bayes conditional independence assumption holds then the Naive Bayes classifier relatively requires little training data as compared to the other discriminative models like logistic regression and hence converges faster. 
  3. It becomes easier to predict the class of test data set with Naive Bayes Classifier algorithm. It is also a good bet for the multi-class predictions too.
  4. Though it needs the conditional independence assumption, the Naive Bayes Classifier presents a good performance in the number of application domains.

2) K Means Clustering Algorithm

K-means unsupervised machine learning algorithm and is widely used for cluster analysis. K-Means Clustering algorithm is a non-deterministic as well as iterative method. The algorithm operates on a specific set of data via a pre-defined number of clusters, k. The output obtained in K Means algorithm is the k clusters with input data divided among the clusters.

For example- let’s take into consideration the K-Means Clustering for Wikipedia Search results. The search term Jaguar on Wikipedia would display all the pages that contain the word Jaguar and refer Jaguar as Mac OS version, Jaguar as a Car and also Jaguar as an Animal. K Means clustering algorithm is also applicable to the group and the web pages that discuss similar concepts. Thus, the algorithm would group all web pages that consider Jaguar as an Animal into one cluster, then  Jaguar as a Car into another cluster and similarly the others.

Benefits of using K-Means Clustering Machine Learning Algorithm

  • K-Means leads to the production of tighter clusters than the hierarchical clustering in case of globular clusters
  • K-Means clustering if given a smaller value of K computes faster in comparison to the hierarchical clustering for the huge number of variables.

Applications of K-Means Clustering

Most of the search engines like Yahoo, Google make use of K Means Clustering algorithm in order to cluster the web pages by similarity and then identify relevance rate of obtained search results. This, in turn, assists the search engines to reduce the computational time for various users.

3) Support Vector Machine Learning Algorithm

Support Vector Machine is a kind of supervised machine learning algorithm used for the classification or regression problems in which the dataset teaches the SVM about classes so that SVM is able to classify any new data. This algorithm works by classifying the data into various classes by finding a line of hyperplane that distinguishes the training data set into classes. SVM algorithm tries to maximize the distance amongst various classes involved due to the existence of hyperplanes and this is known as margin maximization. If the line responsible for maximizing the distance between the various classes is identified, then the probability to generalize the unseen data well increases.

SVM’s are further classified into two different categories:

Linear SVM – In this hyperplane separates the training data i.e. classifiers.

Non-Linear SVM- The training data in non-linear SVM is not separated using a hyperplane. 

For example- the training data for Face detection comprises of a group of images of faces and another group of images other than faces. In such conditions, the training data becomes too complicated that it nearly becomes impossible to find a representation for each feature vector. It is a complex task to separate the set of faces linearly from the set of non-faces.

 Advantages of Using SVM

  • SVM provides complete accuracy on classification performance over the training data.
  • For correct classification of future data, SVM renders more efficiency 
  • The best part about SVM is that it never makes any strong assumptions on data.
  • SVM doesn’t over-fit the data.

Applications of Support Vector Machine

SVM is common finds its application in the stock market forecasting by several financial institutions. For example- SVM can be used for the comparison of the relative performance of the stocks in the similar sector. The relative comparison of stocks assists in managing the investment making decisions that are based upon the classifications that the SVM learning algorithm makes.

Data Science Libraries in Python that implement Support Vector Machine are: SciKit Learn, SVMStruct Python, PyML, LIBSVM

Data Science Libraries in R that implement Support Vector Machine – e1071, klar,

 4) Apriori Machine Learning Algorithm

Apriori algorithm is again a kind of unsupervised machine learning algorithm that is responsible for generating association rules from specific data set. Association rule indicates that if an item A occurs, then with the certain probability item B also occurs. Most of the association rules that generate are in the format IF_THEN. 

For example, IF people purchase an iPad THEN they also purchase an iPad Case for its protection. In order to derive such conclusions, the algorithm initially observes the number of people who have purchased an iPad case while buying an iPad. This way a ratio is obtained such as : out of the 100 people who bought an iPad, 85 of them also bought an iPad case.

The basic principle behind the working of Apriori Machine Learning Algorithm:

  • If an item set frequently repeats then every subset of the item set, also repeats frequently.
  • If an item set does not occur frequently then every superset of the item set also does not have frequent occurrence.

 Advantages of Apriori Algorithm

  • It can be parallelized easily and is simple enough to implement.
  • implementation of the Apriori algorithm makes uses large item set properties.

 Applications of Apriori Algorithm

  •  Detection of Adverse Drug Reactions

Apriori algorithm is utilized for related analysis on healthcare data such as drugs intake by patients, adverse ill-effects that patients experience, characteristics of each patient, initial diagnosis etc. This analysis in turn produces association rules that aid to identify the medications and combination of patient characteristics leading to adverse side effects of the drugs.

  • Market Basket Analysis

Several e-commerce giants including Amazon make use of the Apriori algorithm to gain data insights on which the products are more likely to be bought together and also to find those which are most responsive to the promotion. For example, a retailer may use Apriori algorithm to know whether people who buy sugar and flour are also likely to buy eggs or not.

  • Auto-Complete Applications

Google auto-complete is the other popularly used application of Apriori where, while the user types a word, then the search engine starts looking for other related words that the users generally type after a particular word.

Data Science Libraries in Python that implement Apriori Machine Learning Algorithm is PyPi

Data Science Libraries in R for implementing Apriori Machine Learning Algorithm is rules

 5) Linear Regression Machine Learning Algorithm

Linear Regression algorithm represents the relationship between two variables and the way how a change in any one variable impacts the other. On changing the independent variable the algorithm creates an impact on the dependent variable. The independent variables are also referred to as the explanatory variables because they explain the factors that create an impact on the dependent variable. We often refer to the dependent variable as the factor of interest or a predictor.

Advantages of Linear Regression Machine Learning Algorithm

  • Easy to explain others, Linear Regression is one of the most interpretable machine learning algorithms.
  • It requires minimal tuning and is thus easy to use.
  • It runs faster and is the most widely used machine learning technique 

Applications of Linear Regression

  •  Estimating Sales

 Based on the trends Linear Regression finds significant use in business, for sales forecasting. If a company witnesses steady growth in sales each month – a linear regression analysis of the entire monthly sales data assists the company to forecast sales in the upcoming months.

  • Risk Assessment

 Linear Regression assists determine the risk involved in insurance as well as financial domain. Linear regression can analyze on several claims per customer against age in a health insurance company. This analysis helps the insurance companies to find that older customers go ahead with making more insurance claims. Results of such analysis play a significant role in vital business decisions and are taken to account for risk.

Data Science Libraries in Python for implementing Linear Regression includes: stats model and SciKit

Data Science Libraries in R for implementing Linear Regression includes: stats

These were some of the top machine learning algorithms. Stay tuned to our blog and learn more about the most popular machine learning algorithms, their advantages, and applications!

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