AI/ML Introduction: Episode #9: Top 10 Machine Learning Algorithms
No one machine learning algorithm works best for every problem. Each algorithm has its own strengths and weaknesses, as well as different problems they excel at.
In this blog we will discuss most commonly used machine learning algorithms today.
The top 10 machine learning algorithms are linear regression, multiple linear regression, logistic regression, decision tree, random forest algorithm, support vector machine, k-nearest neighbours, k-means clustering, hierarchical clustering and neural networks.
Let’s now go through each of them in little detail.
#1: Linear Regression:
Linear regression is a powerful tool used in statistical analysis that measures the relationship between two or more variables. It is essentially a way of mapping out the relationship between an independent variable (the predictor) and a dependent variable (the output).
For example, a marketing team may want to understand the relationship between how much they spend on advertising and the amount of revenue generated from those campaigns. Linear regression can help determine which factors are most predictive of revenue, allowing for better decision-making when it comes to budgeting and resource allocation.
#2: Multiple Linear Regression:
Multiple Linear Regression is a powerful tool used to uncover the relationships between several input (independent) variables and an output (dependent) variable. This type of regression is a statistical method that can be used to predict future values of the output variable, given certain combinations of the input variables.
As an example, Multiple Linear Regression can be used to estimate blood pressure based on height, weight, age, and hours of exercise per week. In this case, the output variable would be blood pressure and the input variables would include height, weight, age and number of hours exercising per week. The model can then be used to generate estimates or predictions for any combination of inputs within the range observed while generating the model.
#3: Logistic Regression:
Logistic Regression is a statistical technique used to predict the likelihood of an outcome based on multiple input variables. It is one of the most widely used methods for binary classification, and is particularly useful when trying to predict whether an event will occur (e.g., membership in a class) or not occur (not membership in a class).
Example applications include spam detection, medical diagnosis (such as predicting heart disease), predicting customer churn, loan defaults prediction and predicting employee attrition. It can also be used to build recommendation systems where users are recommended items or services based on past choices or current preferences/behaviors.
#4: Decision Tree:
Decision Trees are a powerful tool used in Machine Learning and Data Science. They are composed of a branching structure that can be used to classify observations based on their features.
For example, if we want to identify whether or not an individual will purchase a product, we could use a Decision Tree to split up the data into different subsets based on age, income level, gender, etc. We could then assign probabilities for each subset according to how likely it is that the individual in that group would purchase the product. This allows us to create predictions about future purchases with greater accuracy than traditional methods such as logistic regression.
#5: Random forest algorithm:
Random forest algorithms are a type of machine learning approach that utilizes an ensemble of decision trees to identify patterns and make predictions. The algorithm works by fitting multiple decision trees to the same training dataset and making predictions with each tree. From the predictions made by these trees, the algorithm takes the majority (or mean) of those votes as its final prediction.
An example application of random forest algorithms is in medical diagnosis. A dataset can be created using patient data such as age, sex, lab tests results, etc., then used to determine if a patient has cancer or not. By combining multiple decision trees which model different parts of the problem space, it’s possible to create a more robust system which is less likely to overfit or underfit the data, leading to better accuracy in diagnosis.
#6: Support Vector Machine:
Support Vector Machine (SVM) is a supervised learning algorithm that is used for both classification and regression tasks. It is mainly used to identify the objects in an image or classify them into different classes. It works by drawing a hyperplane between two or more sets of data points, maximizing the distance between them. This maximized distance is known as the margin, and it helps to better separate one group from another.
One of the most common uses of SVM is face detection. As faces are complex objects with various shapes and sizes, SVM helps to accurately identify which parts of an image are faces and which are not. To do this, it draws a line between those pixels that seem to be part of a face and those pixels that are not part of a face. This ensures accuracy in detecting faces, as most other methods often struggle with detecting small changes in facial features.
#7: K-Nearest Neighbours:
K-Nearest Neighbours (KNN) is a supervised machine learning algorithm used for classification and regression problems. It is a non-parametric method which means that it doesn’t make any assumptions on the underlying data distribution. KNN simply stores all available cases and classifies new cases based on a similarity measure. To determine which class a new case should belong to, KNN considers the k closest cases to the new case in the feature space, and predicts the most frequent class among those k cases.
KNN can be used in various applications such as image recognition, image segmentation, anomaly detection, object tracking, robotics navigation, language translation and recommender systems. For example, in image recognition tasks that involve hand-written digits or photos of objects, KNN can compare images pixel by pixel then classify them into their respective categories based on the majority of similarities among them.
#8: K-means clustering:
K-means clustering is one of the most commonly used unsupervised machine learning algorithms, which is used for data mining and pattern recognition in a variety of applications. K-means clustering is a type of partitioning clustering, which involves dividing a dataset into K clusters, each represented by its own cluster centroid. The cluster centroids are prototypes of the cluster, meaning that they represent the characteristics of the data points within that cluster.
K-means clustering has been applied to numerous applications across different domains, such as customer segmentation in marketing and retail sales analysis, image processing and computer vision tasks such as facial recognition. It can also be used for natural language processing tasks such as clustering words by their semantic similarity.
#9: Hierarchical Clustering:
Hierarchical Clustering is a type of unsupervised learning that groups data points into clusters based on the similarity of their attributes. Hierarchical Clustering typically starts by treating each data point as its own cluster and then gradually merges those clusters together to form larger clusters in a hierarchical manner. This clustering method allows users to explore multidimensional datasets, gain insights from them, and visualize them in meaningful ways.
An example application of Hierarchical Clustering could be grouping loan applicants into different risk categories such as high risk, medium risk, and low risk based on their financial history and other factors. This would allow lenders to better assess loan applications and make more informed decisions.
#10: Neural Network:
Neural Networks are powerful computational models that process data in the same way as a human brain. Neural Network algorithms use multiple layers of neurons to process data. Each neuron has its own weights or parameters that are adjusted during learning. Following training, the network is able to recognize patterns present in the input data or identify anomalous activities that may indicate fraud.
For example, Neural Networks can also be used for anomaly detection and fraud identification solutions which can detect unusual activity from regular behavioural patterns from financial institutions, online businesses and other organisations who rely on trust from customers–such as banks detecting suspicious transactions from valid card holders.
Conclusion:
In conclusion, there are a variety of machine learning algorithms available, each with its own strengths and weaknesses.
Each algorithm has a different set of capabilities and can be used for various applications, ranging from image recognition to anomaly detection. With the right selection of algorithm and good data preparation, powerful solutions can be developed which have the potential to provide valuable insights from complex datasets.
