10 Fundamental Machine Learning Models that You Must Know

Machine learning has become prevalent over the last few years and it is widely applied across different domains. Health care, marketing, insurance and finance among others have witness the dominant force of this prevailing technology. Amazon for instance is heavily reliant on recommendation systems. Search engines learn to craw websites by reason of this technology. Attempting to predict the probability of a customer defaulting on loan payment is a mechanisms of machine learning.

This technology has become a necessity in the world but most importantly, to data scientist, who need to understand the fundamental workings of machine learning algorithms with the hopes of developing models that can become applicable in real world situations.

So, what is machine learning?

Machine learning is a branch of artificial intelligence that involves training a set of mathematical and statistical instructions on data to recognize patterns or make predictions. The process is known as an algorithm, and the output of this algorithm is referred to generally as a model. This is a simplified definition of a model, subsequently, it is vital to realize that before training an algorithm on data there are other processes such; as preprocessing the data, feature selection and evaluating the performance of the model.

Model evaluation suggest the idea that models are fallible can output incorrect result particularly where models are trained improperly or where the data is biased. There are different kinds of models, each with their strength and limitations. Therefore, a model must be selected in relation to the problem you are solving for.

The two categories of machine learning

But before then, we must understand that machine learning is grouped into two broad categories: supervised and unsupervised learning. Supervised learning employs labels to train algorithms while unsupervised learning targets unstructured data. The next section will briefly discus some machine models and when to apply these models. Here are 10 fundamental machine learning models that you should know:

Linear Regression: Used for regression tasks, it models the relationship between a dependent variable and one or more independent variables. An instance of a linear regression can be modelled in the relationship between a student’s test score and the number of study hours. A linear regression can establish this fact or prove the assumption.

Logistic Regression: Used for classification tasks, it predicts the probability of an event occurring. So that whether a customer will sign up for the service of a meals and wheels service after a period of trial can be predicted by a logistic regression.

Decision Trees: Geared towards both regression and classification tasks, it models after a real where each branch of the tree represents a decision and the leaf, the corresponding outcome. Here is an example. Suppose you want to buy a house. You would first consider the price, then the neighbourhood, type of the house, condition of the home, age of the property, and utility bills etc.

This can be visually represented in a decision tree. The decision tree can be created by starting with a root node that represents the initial decision to buy a house. The tree then branches out into different nodes that represent different factors to consider, such as location, affordability, and type of the house. Each node has a set of rules that determine the next decision to make. For example, if the location is not suitable, the decision tree may lead to the conclusion that the house is not worth buying.

Random Forest: A collection of decision trees, it improves the accuracy and robustness of the model. Random Forest is a machine learning algorithm that can be used to predict the price of a book. For example, a random forest model can be trained on a dataset of book sales history data to predict the price of a book based on various features such as author, genre, and publication date.

The model works by creating multiple decision trees and combining their predictions to make a more accurate prediction. Each decision tree is trained on a random subset of the data, and the final prediction is made by averaging the predictions of all the trees. Random Forest can also be used to predict academic success and major choice of university students based on various factors such as student demographics, high school-level factors, and course completion.

Support Vector Machines (SVM): Used for classification and regression tasks, it separates data points into different classes or predicts a continuous value. A typical example can be found in the meals and wheels sector. Support Vector Machines (SVM) can be used to classify and predict the nutritional value of meals delivered to seniors.

SVM is a machine learning algorithm that separates data points into different classes or predicts a continuous value. In this case, SVM can be trained on a dataset of meal nutritional values to classify meals based on their nutritional content. The model can then be used to predict the nutritional value of new meals based on their ingredients and nutritional information. This can help Meals on Wheels ensure that they are delivering nutritious meals to seniors and meeting their dietary needs.

Naive Bayes: Based on Bayes' theorem, it is used for classification tasks and assumes that the features are independent of each other. Naive Bayes is a machine learning algorithm that can be used in similarity to (SVM) above by Meals on Wheels company.

It can be used to classify meals based on their nutritional content. In this case, Naive Bayes can be trained on a dataset of meal nutritional values to classify meals based on their nutritional content. The model can then be used to predict the nutritional value of new meals based on their ingredients and nutritional information. This can help Meals on Wheels ensure that they are delivering nutritious meals to seniors and meeting their dietary needs.

K-Nearest Neighbour’s (KNN): Geared towards classification and regression tasks, it predicts the value of a data point based on the values of its k-nearest neighbours. K-Nearest Neighbour’s (KNN) is a simple machine learning algorithm used for classification and regression tasks.

It predicts the value of a data point based on the values of its k-nearest neighbours. KNN is used in various areas such as image recognition, video recognition, and handwriting detection. The algorithm is based on the principle of "information gain" and is used to predict an unknown value. KNN is a memory-based algorithm and cannot be summarized by a closed-form model.

Learning Vector Quantization (LVQ): A supervised learning algorithm used for classification tasks, it maps input vectors to a discrete number of output classes.

Learning Vector Quantization (LVQ) is a supervised machine learning algorithm that maps input vectors to a discrete number of output classes. It is based on a prototype supervised learning classification algorithm and trained through a competitive learning algorithm similar to Self-Organizing Map. LVQ can deal with the multiclass classification problem and has two layers, Input layer and Output layer.

The algorithm determines the winner prototype closest to the input according to a given distance measure. The position of the winner prototype is then adapted, and the winner is moved closer if it correctly classifies the data point or moved away if it classifies the data point incorrectly.

Gradient Boosting Machine (GBM): A boosting algorithm that combines multiple weak models to create a strong model, it is used for regression and classification tasks. Gradient Boosting Machine (GBM) is a powerful machine learning algorithm used for regression and classification tasks.

It combines multiple weak models to create a strong model. GBM works by minimizing the loss function of the model by adding weak learners using gradient descent. It generates learners during the learning process, and the contribution of the weak learner to the ensemble is based on the gradient descent optimization process. GBM is used in various areas such as image recognition, video recognition, and handwriting detection.

Neural Networks: Inspired by the structure of the human brain, they are used for various tasks such as image recognition, natural language processing, and time series forecasting.

Neural Networks are machine learning algorithms inspired by the human brain structure. They are used for various tasks such as image recognition, natural language processing, and time series forecasting. Convolutional Neural Networks (CNNs) are a type of neural network used for image recognition and object classification.

Neural Networks are composed of artificial neurons and can be trained to perform specific tasks such as clustering, classification, and pattern recognition. They are used in various industries such as healthcare, eCommerce, entertainment, and advertising.

In conclusion, these 10 models are just a glimpse of the vast landscape of machine learning. As the field continues to evolve, new models and techniques are being developed to tackle complex problems and improve the accuracy and efficiency of predictions and pattern recognition.

They are: linear regression, logistic regression, decision Trees, random forest, Support Vector Machines (SVM), Naive Bayes, K-Nearest Neighbour’s (KNN), Learning Vector Quantization (LVQ), Gradient Boosting Machine (GBM) and Neural Networks