Machine Learning Algorithms Every Data Scientist Should Know

Machine Learning Algorithms Every Data Scientist Should Know

Types Of ML Algorithms

There are a huge number of ML algorithms out there. Trying to classify them leads to the distinction being made in types of the training procedure, applications, the latest advances, and some of the standard algorithms used by ML scientists in their daily work. There is a lot to cover, and we shall proceed as given in the following listing:

  1. Statistical Algorithms
  2. Classification
  3. Regression
  4. Clustering
  5. Dimensionality Reduction
  6. Ensemble Algorithms
  7. Deep Learning
  8. Reinforcement Learning
  9. AutoML (Bonus)

1. Statistical Algorithms

Statistics is necessary for every machine learning expert. Hypothesis testing and confidence intervals are some of the many statistical concepts to know if you are a data scientist. Here, we consider here the phenomenon of overfitting. Basically, overfitting occurs when an ML model learns so many features of the training data set that the generalization capacity of the model on the test set takes a toss. The tradeoff between performance and overfitting is well illustrated by the following illustration:

Overfitting - from Wikipedia

Overfitting – from Wikipedia

 

Here, the black curve represents the performance of a classifier that has appropriately classified the dataset into two categories. Obviously, training the classifier was stopped at the right time in this instance. The green curve indicates what happens when we allow the training of the classifier to ‘overlearn the features’ in the training set. What happens is that we get an accuracy of 100%, but we lose out on performance on the test set because the test set will have a feature boundary that is usually similar but definitely not the same as the training set. This will result in a high error level when the classifier for the green curve is presented with new data. How can we prevent this?

Cross-Validation

Cross-Validation is the killer technique used to avoid overfitting. How does it work? A visual representation of the k-fold cross-validation process is given below:

From Quora

The entire dataset is split into equal subsets and the model is trained on all possible combinations of training and testing subsets that are possible as shown in the image above. Finally, the average of all the models is combined. The advantage of this is that this method eliminates sampling error, prevents overfitting, and accounts for bias. There are further variations of cross-validation like non-exhaustive cross-validation and nested k-fold cross validation (shown above). For more on cross-validation, visit the following link.

There are many more statistical algorithms that a data scientist has to know. Some examples include the chi-squared test, the Student’s t-test, how to calculate confidence intervals, how to interpret p-values, advanced probability theory, and many more. For more, please visit the excellent article given below:

Learning Statistics Online for Data Science

2. Classification Algorithms

Classification refers to the process of categorizing data input as a member of a target class. An example could be that we can classify customers into low-income, medium-income, and high-income depending upon their spending activity over a financial year. This knowledge can help us tailor the ads shown to them accurately when they come online and maximises the chance of a conversion or a sale. There are various types of classification like binary classification, multi-class classification, and various other variants. It is perhaps the most well known and most common of all data science algorithm categories. The algorithms that can be used for classification include:

  1. Logistic Regression
  2. Support Vector Machines
  3. Linear Discriminant Analysis
  4. K-Nearest Neighbours
  5. Decision Trees
  6. Random Forests

and many more. A short illustration of a binary classification visualization is given below:

binary classification visualization

From openclassroom.stanford.edu

 

For more information on classification algorithms, refer to the following excellent links:

How to train a decision tree classifier for churn prediction

3. Regression Algorithms

Regression is similar to classification, and many algorithms used are similar (e.g. random forests). The difference is that while classification categorizes a data point, regression predicts a continuous real-number value. So classification works with classes while regression works with real numbers. And yes – many algorithms can be used for both classification and regression. Hence the presence of logistic regression in both lists. Some of the common algorithms used for regression are

  1. Linear Regression
  2. Support Vector Regression
  3. Logistic Regression
  4. Ridge Regression
  5. Partial Least-Squares Regression
  6. Non-Linear Regression

For more on regression, I suggest that you visit the following link for an excellent article:

Multiple Linear Regression & Assumptions of Linear Regression: A-Z

Another article you can refer to is:

Logistic Regression: Concept & Application

Both articles have a remarkably clear discussion of the statistical theory that you need to know to understand regression and apply it to non-linear problems. They also have source code in Python and R that you can use.

4. Clustering

Clustering is an unsupervised learning algorithm category that divides the data set into groups depending upon common characteristics or common properties. A good example would be grouping the data set instances into categories automatically, the process being used would be any of several algorithms that we shall soon list. For this reason, clustering is sometimes known as automatic classification. It is also a critical part of exploratory data analysis (EDA). Some of the algorithms commonly used for clustering are:

  1. Hierarchical  Clustering – Agglomerative
  2. Hierarchical Clustering – Divisive
  3. K-Means Clustering
  4. K-Nearest Neighbours Clustering
  5. EM (Expectation Maximization) Clustering
  6. Principal Components Analysis Clustering (PCA)

An example of a common clustering problem visualization is given below:

clustering problem visualization

From Wikipedia

 

The above visualization clearly contains three clusters.

Another excellent article on clustering refer the link

You can also refer to the following article:

 

ML Methods for Prediction and Personalization

5. Dimensionality Reduction

Dimensionality Reduction is an extremely important tool that should be completely clear and lucid for any serious data scientist. Dimensionality Reduction is also referred to as feature selection or feature extraction. This means that the principal variables of the data set that contains the highest covariance with the output data are extracted and the features/variables that are not important are ignored. It is an essential part of EDA (Exploratory Data Analysis) and is nearly always used in every moderately or highly difficult problem. The advantages of dimensionality reduction are (from Wikipedia):

  1. It reduces the time and storage space required.
  2. Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model.
  3. It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.
  4. It avoids the curse of dimensionality.

The most commonly used algorithm for dimensionality reduction is Principal Components Analysis or PCA. While this is a linear model, it can be converted to a non-linear model through a kernel trick similar to that used in a Support Vector Machine, in which case the technique is known as Kernel PCA. Thus, the algorithms commonly used are:

  1. Principal Component Analysis (PCA)
  2. Non-Negative Matrix Factorization (NMF)
  3. Kernel PCA
  4. Linear Discriminant Analysis (LDA)
  5. Generalized Discriminant Analysis (kernel trick again)

The result of a  is visualized below:

PCA operation visulaization

By Nicoguaro – Own work, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=46871195

 

You can refer to this article for a general discussion of dimensionality reduction:

This article below gives you a brief description of dimensionality reduction using PCA by coding an ML example:

MULTI-VARIATE ANALYSIS

6. Ensembling Algorithms

Ensembling means combining multiple ML learners together into one pipeline so that the combination of all the weak learners makes an ML application with higher accuracy than each learner taken separately. Intuitively, this makes sense, since the disadvantages of using one model would be offset by combining it with another model that does not suffer from this disadvantage. There are various algorithms used in ensembling machine learning models. The three common techniques usually employed in  practice are:

  1. Simple/Weighted Average/Voting: Simplest one, just takes the vote of models in Classification and average in Regression.
  2. Bagging: We train models (same algorithm) in parallel for random sub-samples of data-set with replacement. Eventually, take an average/vote of obtained results.
  3. Boosting: In this models are trained sequentially, where (n)th model uses the output of (n-1)th model and works on the limitation of the previous model, the process stops when result stops improving.
  4. Stacking: We combine two or more than two models using another machine learning algorithm.

(from Amardeep Chauhan on Medium.com)

In all four cases, the combination of the different models ends up having the better performance that one single learner. One particular ensembling technique that has done extremely well on data science competitions on Kaggle is the GBRT  model or the Gradient Boosted Regression Tree model.

 

We include the source code from the scikit-learn module for Gradient Boosted Regression Trees since this is one of the most popular ML models which can be used in competitions like Kaggle, HackerRank, and TopCoder.

Refer Link here

GradientBoostingClassifier supports both binary and multi-class classification. The following example shows how to fit a gradient boosting classifier with 100 decision stumps as weak learners:


 

GradientBoostingRegressor supports a number of different loss functions for regression which can be specified via the argument loss; the default loss function for regression is least squares ('ls').


 

You can also refer to the following article which discusses Random Forests, which is a (rather basic) ensembling method.

Introduction to Random forest

 

7. Deep Learning

In the last decade, there has been a renaissance of sorts within the Machine Learning community worldwide. Since 2002, neural networks research had struck a dead end as the networks of layers would get stuck in local minima in the non-linear hyperspace of the energy landscape of a three layer network. Many thought that neural networks had outlived their usefulness. However, starting with Geoffrey Hinton in 2006, researchers found that adding multiple layers of neurons to a neural network created an energy landscape of such high dimensionality that local minima were statistically shown to be extremely unlikely to occur in practice. Today, in 2019, more than a decade of innovation later, this method of adding addition hidden layers of neurons to a neural network is the classical practice of the field known as deep learning.

Deep Learning has truly taken the computing world by storm and has been applied to nearly every field of computation, with great success. Now with advances in Computer Vision, Image Processing, Reinforcement Learning, and Evolutionary Computation, we have marvellous feats of technology like self-driving cars and self-learning expert systems that perform enormously complex tasks like playing the game of Go (not to be confused with the Go programming language). The main reason these feats are possible is the success of deep learning and reinforcement learning (more on the latter given in the next section below). Some of the important algorithms and applications that data scientists have to be aware of in deep learning are:

  1. Long Short term Memories (LSTMs) for Natural Language Processing
  2. Recurrent Neural Networks (RNNs) for Speech Recognition
  3. Convolutional Neural Networks (CNNs) for Image Processing
  4. Deep Neural Networks (DNNs) for Image Recognition and Classification
  5. Hybrid Architectures for Recommender Systems
  6. Autoencoders (ANNs) for Bioinformatics, Wearables, and Healthcare

 

Deep Learning Networks typically have millions of neurons and hundreds of millions of connections between neurons. Training such networks is such a computationally intensive task that now companies are turning to the 1) Cloud Computing Systems and 2) Graphical Processing Unit (GPU) Parallel High-Performance Processing Systems for their computational needs. It is now common to find hundreds of GPUs operating in parallel to train ridiculously high dimensional neural networks for amazing applications like dreaming during sleep and computer artistry and artistic creativity pleasing to our aesthetic senses.

 

Artistic Image Created By A Deep Learning Network

Artistic Image Created By A Deep Learning Network. From blog.kadenze.com.

 

For more on Deep Learning, please visit the following links:

Machine Learning and Deep Learning : Differences

For information on a full-fledged course in deep learning, visit the following link:

Deep Learning

8. Reinforcement Learning (RL)

In the recent past and the last three years in particular, reinforcement learning has become remarkably famous for a number of achievements in cognition that were earlier thought to be limited to humans. Basically put, reinforcement learning deals with the ability of a computer to teach itself. We have the idea of a reward vs. penalty approach. The computer is given a scenario and ‘rewarded’ with points for correct behaviour and ‘penalties’ are imposed for wrong behaviour. The computer is provided with a problem formulated as a Markov Decision Process, or MDP. Some basic types of Reinforcement Learning algorithms to be aware of are (some extracts from Wikipedia):

 

1.Q-Learning

Q-Learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. “Q” names the function that returns the reward used to provide the reinforcement and can be said to stand for the “quality” of an action taken in a given state.

 

2.SARSA

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy. This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent “S1“, the action the agent chooses “A1“, the reward “R” the agent gets for choosing this action, the state “S2” that the agent enters after taking that action, and finally the next action “A2” the agent choose in its new state. The acronym for the quintuple (st, at, rt, st+1, at+1) is SARSA.

 

3.Deep Reinforcement Learning

This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. Remarkably, the computer agent DeepMind has achieved levels of skill higher than humans at playing computer games. Even a complex game like DOTA 2 was won by a deep reinforcement learning network based upon DeepMind and OpenAI Gym environments that beat human players 3-2 in a tournament of best of five matches.

For more information, go through the following links:

Reinforcement Learning: Super Mario, AlphaGo and beyond

and

How to Optimise Ad CTR with Reinforcement Learning

 

Finally:

9. AutoML (Bonus)

If reinforcement learning was cutting edge data science, AutoML is bleeding edge data science. AutoML (Automated Machine Learning) is a remarkable project that is open source and available on GitHub at the following link that, remarkably, uses an algorithm and a data analysis approach to construct an end-to-end data science project that does data-preprocessing, algorithm selection,hyperparameter tuning, cross-validation and algorithm optimization to completely automate the ML process into the hands of a computer. Amazingly, what this means is that now computers can handle the ML expertise that was earlier in the hands of a few limited ML practitioners and AI experts.

AutoML has found its way into Google TensorFlow through AutoKeras, Microsoft CNTK, and Google Cloud Platform, Microsoft Azure, and Amazon Web Services (AWS). Currently it is a premiere paid model for even a moderately sized dataset and is free only for tiny datasets. However, one entire process might take one to two or more days to execute completely. But at least, now the computer AI industry has come full circle. We now have computers so complex that they are taking the machine learning process out of the hands of the humans and creating models that are significantly more accurate and faster than the ones created by human beings!

The basic algorithm used by AutoML is Network Architecture Search and its variants, given below:

  1. Network Architecture Search (NAS)
  2. PNAS (Progressive NAS)
  3. ENAS (Efficient NAS)

The functioning of AutoML is given by the following diagram:

how autoML works

From cloud.google.com

 

For more on AutoML, please visit the link

and

Top 10 Artificial Intelligence Trends in 2019

 

If you’ve stayed with me till now, congratulations; you have learnt a lot of information and cutting edge technology that you must read up on, much, much more. You could start with the links in this article, and of course, Google is your best friend as a Machine Learning Practitioner. Enjoy machine learning!

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course, which is a step further into advanced data analysis and processing!

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Spam Detection with Natural Language Processing (NLP) – Part 1

     Part 1: Data Cleaning and Exploratory Data Analysis

Spam detection with NLP

Predicting whether an SMS is a spam

 

Natural language processing (NLP) is a subfield of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages.

When I first began learning NLP, it was difficult for me to process text and generate insights out of it. Before actually diving deep into NLP, I knew some of the basic techniques in NLP before but never could connect them together to view it as an end to end process of generating insights out of the text data.

In this blog, we will try to build a simple classifier using machine learning which will help in identifying whether a given SMS is a spam or not. Parallely, we will also be understanding a few basic components of Natural Language Processing (NLP) for the readers who are new to natural language processing.

Building SMS SPAM Classifier

In this section, we will be building a spam classifier step by step.

Step 1: Importing Libraries

We will be using pandas, numpy and Multinomial naive Bayes classifier for building a spam detector. Pandas will be used for performing operations on data frames. Furthermore using numpy, we will perform necessary mathematical operations.

Step 2: Reading the dataset and preparing it for basic processing in NLP

First, we read the csv using pandas read_csv function. We then modify the column names for easy references. In this dataset, the target variable is categorical (ham, spam) and we need to convert into a binary variable. Remember, machine learning models always take numbers as input and not the text hence we need to convert all categorical variables into numerical ones.

We replace ham with 0 (meaning not a spam) and spam with 1 (meaning that the SMS is a spam)

Step 3: Cleaning Data

Well, Cleaning text is one of the interesting and very important steps before performing any kind of analysis over it. Text from social media and another platform may contain many irregularities in it. People tend to express their feeling while writing and you may end up with words like gooood or goood or goooooooooooood in your dataset. Essentially all are same but we need to regularize this data first. I have made a function below which works fairly well in removing all the inconsistencies from the data.

Clean_data() function takes a sentence as it’s input and returns a cleaned sentence. This function takes care of the following

  1. Removing web links from the text data as they are not pretty much useful
  2. Correcting words like poooor and baaaaaad to poor and bad
  3. Removing punctuations from the text
  4. Removing apostrophes from the text to correct words like I’m to I am
  5. Correcting spelling mistakes

Below is the snippet for clean_data function

Function to remove punctuations from the sentence

Function to remove apostrophes from the sentences

Example of using the clean_data function

Now in order to process and clean all the text data in our dataset, we iterate over every text in the dataset and apply the  clean_data function to retriever cleaner texts

Step 4: Understanding text data and finding Important words

After cleaning our text data, we want to analyze it but how de analyze text data? In the case of numbers, we could have gone with finding out mean, median, standard deviation, and other statistics to understand the data but how do we go about here?

We can not take a whole sentence up and generate meaning from it. Although, we can take words from those sentences and try to find out words that are frequently occurring in the text document or finding out the words which hold relatively higher importance in helping us understand what the complete sentence is about. In case of identifying a message as spam, we need to understand that are there any specific words or sequence of words that determine whether an SMS is a spam or not.

Tokenization and Lemmatization

We start by breaking each sentence into individual words. So a sentence like “Hey, You are awesome” will be broken into individual words into an array [‘hey’, ‘you’, ‘are’, ‘awesome’]. This process is known as tokenization and every single word is known as tokens. After getting each token, we try to get each token into its most basic form. For example, words like studies and goes will become study and go respectively. Also, remember that we need to remove stopwords like I, you, her, him etc as these words are very frequent in the text and hardly lead to any interpretation about any message being a spam or not!

Given below, I have made a tokenizer function which will take each sentence as input. It splits the sentence into individual tokens and then lemmatizes those words. In the end, we remove stop words from the tokens we have and return these tokens as an array.

Example showing the working of my_tokeniser function

Understanding n-grams

An n-gram is a contiguous sequence of n items from a given sequence of text. Given a sentence, swe can construct a list of n-grams from s finding pairs of words that occur next to each other. For example, given the sentence “I am Kartik” you can construct bigrams (n-grams of length 2) by finding consecutive pairs of words which will be (“I”, “am”), (“am”, “Kartik”).

A consecutive pair of three words is known as tri-grams. This will help us to understand how exactly a sequence of tokens together determines whether an incoming message is a spam or not. In natural language processing (NLP), n-grams hold a lot of importance as they determine how sequences of words affect the meaning of a sentence.

We will be finding out most common bi-grams and tri-grams from the messages we have in the dataset separately for both spam and non-spam messages and consecutively will have a look at most commonly occurring sequences of text in each category.

Code for finding out bi-grams and tri-grams

Below is a python function which takes two input parameters i.e. label and n. The “label” parameter is the target label of the message. For spam messages, it is 1 whereas for non-spam messages it is 0. The “n” parameter is for selecting whether we want to extract bi-grams out or tri-grams out from the sentences. A too much high value for n will not make any sense as long sequences of text are majorly not common throughout the data

We will call the below function to directly plot all the common bigrams or trigrams as a word cloud. This function calls the above function to get all the bi_grams or tri_grams from the messages we have and will then plot it

Most frequent words in spam messages

word cloud result of spam detection NLP model - 2

Most frequent words in non-spam messages

word cloud result of spam detection NLP model

Top 15 frequent bigrams for non-spam messages

result of ML algorithm - 1

Top 15 frequent bigrams for spam messages

result of ML algorithm - 2

Visualizing most frequent trigrams for non-spam messages

result of ML algorithm - 3

Visualizing most frequent trigrams for spam messages

result of ML algorithm - 4
Conclusion

Till now we have learned how to start with cleaning and understanding data. This process needs to be done before any kind of text analysis. One should always start with cleaning the text and then move on to fetch tokens out of the text. Getting tokens out of the text also requires to exclude stop words. Also, we need to get all the other words into their basic morphological form using lemmatization. In the next blog, we will have a look at finding out important words from the text data. We will also learn the word embeddings. In the end, we will finally build a classifier to segregate spam SMS out.

Stay tuned!