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Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, the text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Machine Learning Algorithms
An “algorithm” in machine learning is a procedure that is run on data to create a machine learning “model.” Machine learning algorithms perform “pattern recognition.” Algorithms “learn” from data, or are “fit” on a dataset. There are many machine learning algorithms. For example, we have algorithms for classification, such as k-nearest neighbors. We have algorithms for regression, such as linear regression, and we have algorithms for clustering, such as k-means.
What is a “Model” in Machine Learning?
A “model” in machine learning is the output of a machine learning algorithm run on data.
A model represents what was learned by a machine learning algorithm.
The model is the “thing” that is saved after running a machine learning algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.
Some examples might make this clearer:
- The linear regression algorithm results in a model comprised of a vector of coefficients with specific values.
- The decision tree algorithm results in a model comprised of a tree of if-then statements with specific values.
- The neural network/backpropagation/gradient descent algorithms together result in a model comprised of a graph structure with vectors or matrices of weights with specific values.
A machine learning model is more challenging for a beginner because there is not a clear analogy with other algorithms in computer science.
For example, the sorted list output of a sorting algorithm is not really a model.
The best analogy is to think of the machine learning model as a “program.”
The machine learning model “program” is comprised of both data and a procedure for using the data to make a prediction.
For example, consider the linear regression algorithm and the resulting model. The model is comprised of a vector of coefficients (data) that are multiplied and summed with a row of new data taken as input in order to make a prediction (prediction procedure).
We save the data for the machine learning model for later use.
We often use the prediction procedure for the machine learning model provided by a machine learning library. Sometimes we may implement the prediction procedure ourselves as part of our application. This is often straightforward to do given that most prediction procedures are quite simple.
Commonly Used Machine Learning Algorithms
Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
1. Linear Regression
It is used to estimate real values (cost of houses, number of calls, total sales, etc.) based on a continuous variable(s). Here, we establish a relationship between independent and dependent variables by fitting the best line. This best fit line is known as the regression line and is represented by a linear equation – Y = a × X + b.
The best way to understand linear regression is to relive this experience of childhood. Let us say, you ask a child in fifth grade to arrange people in his class by increasing order of weight, without asking them their weights! What do you think the child will do? She/he would likely look (visually analyze) at the height and build of people and arrange them using a combination of these visible parameters. This is linear regression in real life! The child has actually figured out that height and build would be correlated to the weight by a relationship, which looks like the equation above.
In this equation:
- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept
These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line. Look at the below example. Here we have identified the best fit line having linear equation y=0.0.3874x+15.87. Now using this equation, we can find the weight, knowing the height of a person.
Linear Regression is mainly of two types:
- Simple Linear Regression
- Multiple Linear Regression
Simple Linear Regression is characterized by one independent variable. And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding the best fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression.
2. Logistic Regression
In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead, or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object is detected in the image would be assigned a probability between 0 and 1, with a sum of one.
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled “0” and “1”. In the logistic model, the log-odds (the logarithm of the odds) for the value labeled “1” is a linear combination of one or more independent variables (“predictors”); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value).
The corresponding probability of the value labeled “1” can vary between 0 (certainly the value “0”) and 1 (certainly the value “1”), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from the logistic unit, hence the alternative names.
Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable, this generalizes the odds ratio.
In a binary logistic regression model, the dependent variable has two levels (categorical). Outputs with more than two values are modeled by multinomial logistic regression and, if the multiple categories are ordered, by ordinal logistic regression (for example the proportional odds ordinal logistic model).
The logistic regression model itself simply models the probability of output in terms of input and does not perform statistical classification (it is not a classifier), though it can be used to make a classifier, for instance by choosing a cutoff value and classifying inputs with probability greater than the cutoff as one class, below the cutoff as the other; this is a common way to make a binary classifier.
3. Decision Tree
A Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.
In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches.
The decisions or the test are performed on the basis of features of the given dataset.
It is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions.
It is called a decision tree because similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure. In order to build a tree, we use the CART algorithm, which stands for Classification and Regression Tree algorithm. A decision tree simply asks a question and based on the answer (Yes/No), it further split the tree into subtrees.
Below diagram explains the general structure of a decision tree:
Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.
The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.
SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called support vectors, and hence algorithm is termed a Support Vector Machine. Consider the below diagram in which there are two different categories that are classified using a decision boundary or hyperplane:
Consider the following example. SVM can be understood with the example that we have used in the KNN classifier. Suppose we see a strange cat that also has some features of dogs, so if we want a model that can accurately identify whether it is a cat or dog, so such a model can be created by using the SVM algorithm. We will first train our model with lots of images of cats and dogs so that it can learn about different features of cats and dogs, and then we test it with this strange creature. So as the support vector creates a decision boundary between these two data (cat and dog) and chooses extreme cases (support vectors), it will see the extreme case of cat and dog. On the basis of the support vectors, it will classify it as a cat.
5. Naive Bayes
Naïve Bayes algorithm is a supervised learning algorithm, which is based on the Bayes theorem and used for solving classification problems. It is mainly used in text classification that includes a high-dimensional training dataset.
Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
Some popular examples of the Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.
The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, Which can be described as:
- Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features. Such as if the fruit is identified on the basis of color, shape, and taste, then red, spherical, and sweet fruit is recognized as an apple. Hence each feature individually contributes to identifying that it is an apple without depending on each other.
- Bayes: It is called Bayes because it depends on the principle of Bayes’ Theorem.
- Bayes’ theorem is also known as Bayes’ Rule or Bayes’ law, which is used to determine the probability of a hypothesis with prior knowledge. It depends on conditional probability.
- The formula for Bayes’ theorem is given as:
P(A|B) is Posterior probability: Probability of hypothesis A on the observed event B.P(B|A) is Likelihood probability: Probability of the evidence given that the probability of a hypothesis is true.
K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on the Supervised Learning technique. K-NN algorithm assumes the similarity between the new case/data and available cases and puts the new case into the category that is most similar to the available categories.
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a good suite category by using K- NN algorithm. This algorithm can be used for Regression as well as for Classification but mostly it is used for Classification problems.
It is a non-parametric algorithm, which means it does not make any assumptions on underlying data. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset.
KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data.
For example suppose, we have an image of a creature that looks similar to a cat and dog, but we want to know either it is a cat or dog. So for this identification, we can use the KNN algorithm, as it works on a similarity measure. Our KNN model will find the similar features of the new data set to the cats and dogs images and based on the most similar features it will put it in either cat or dog category.
It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups.
Remember figuring out shapes from inkblots? k means is somewhat similar to this activity. You look at the shape and spread to decipher how many different clusters/populations are present!
K-Means uses the following steps:
- K-means picks k number of points for each cluster known as centroids.
- Each data point forms a cluster with the closest centroids i.e. k clusters.
- Finds the centroid of each cluster based on existing cluster members. Here we have new centroids.
- As we have new centroids, repeat steps 2 and 3. Find the closest distance for each data point from new centroids and get associated with new k-clusters. Repeat this process until convergence occurs i.e. centroids do not change.
In K-means, we have clusters and each cluster has its own centroid. The sum of the square of the difference between the centroid and the data points within a cluster constitutes within the sum of the square value for that cluster. Also, when the sum of square values for all the clusters is added, it becomes a total within the sum of the square value for the cluster solution.
We know that as the number of clusters increases, this value keeps on decreasing but if you plot the result you may see that the sum of squared distance decreases sharply up to some value of k, and then much more slowly after that. Here, we can find the optimum number of clusters.
8. Random Forest
Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.
As the name suggests, “Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.” Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output.
The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.
The below diagram explains the working of the Random Forest algorithm:
9. Dimensionality Reduction Algorithms
Dimensionality reduction is an unsupervised learning technique.
Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms.
There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm.
Dimensionality reduction refers to techniques for reducing the number of input variables in training data.
High-dimensionality might mean hundreds, thousands, or even millions of input variables.
Fewer input dimensions often mean correspondingly fewer parameters or a simpler structure in the machine learning model, referred to as degrees of freedom. A model with too many degrees of freedom is likely to overfit the training dataset and may not perform well on new data.
It is desirable to have simple models that generalize well, and in turn, input data with few input variables. This is particularly true for linear models where the number of inputs and the degrees of freedom of the model is often closely related.
Dimensionality reduction is a data preparation technique performed on data prior to modeling. It might be performed after data cleaning and data scaling and before training a predictive model.
As such, any dimensionality reduction performed on training data must also be performed on new data, such as a test dataset, validation dataset, and data when making a prediction with the final model.
There are many algorithms that can be used for dimensionality reduction.
Two main classes of methods are those drawn from linear algebra and those drawn from manifold learning.
Linear Algebra Models
Matrix factorization methods drawn from the field of linear algebra can be used for dimensionality.
Some of the more popular methods include:
- Principal Components Analysis
- Singular Value Decomposition
- Non-Negative Matrix Factorization
Manifold Learning Methods
Manifold learning methods seek a lower-dimensional projection of high-dimensional input that captures the salient properties of the input data.
Some of the more popular methods include:
- Isomap Embedding
- Locally Linear Embedding
- Multidimensional Scaling
- Spectral Embedding
- t-distributed Stochastic Neighbor Embedding
Each algorithm offers a different approach to the challenge of discovering natural relationships in data at lower dimensions.
There is no best dimensionality reduction algorithm, and no easy way to find the best algorithm for your data without using controlled experiments.
10. Gradient Boosting Algorithms
The term gradient boosting consists of two sub-terms, gradient and boosting. We already know that gradient boosting is a boosting technique. Let us see how the term ‘gradient’ is related here.
Gradient boosting re-defines boosting as a numerical optimization problem where the objective is to minimize the loss function of the model by adding weak learners using gradient descent. Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. As gradient boosting is based on minimizing a loss function, different types of loss functions can be used resulting in a flexible technique that can be applied to regression, multi-class classification, etc.
Intuitively, gradient boosting is a stage-wise additive model that generates learners during the learning process (i.e., trees are added one at a time, and existing trees in the model are not changed). The contribution of the weak learner to the ensemble is based on the gradient descent optimization process. The calculated contribution of each tree is based on minimizing the overall error of the strong learner.
Gradient boosting does not modify the sample distribution as weak learners train on the remaining residual errors of a strong learner (i.e, pseudo-residuals). By training on the residuals of the model, is an alternative means to give more importance to misclassified observations. Intuitively, new weak learners are being added to concentrate on the areas where the existing learners are performing poorly. The contribution of each weak learner to the final prediction is based on a gradient optimization process to minimize the overall error of the strong learner.