Uncovering the Possibilities in Artificial Intelligence- an in-Depth Look. Part II.
Introduction
In Chapter I we studied that machine learning is a field of artificial intelligence (AI) that involves:
The development of algorithms and statistical models enables computer systems to learn and make predictions or decisions based on data without being explicitly programmed.
In machine learning, computer systems are trained to identify patterns in large data sets (big data) and make predictions or decisions based on these patterns.
Algorithms iteratively learn from the data, adjusting their parameters in response to feedback, until they can make accurate predictions or decisions.
There are several types of machine learning:
Supervised learning.
Unsupervised learning.
Reinforcement learning.
In this chapter, we will study what is supervised machine learning (SML).
Supervised machine learning:
A model in which an algorithm is trained on a set of labeled data to predict or classify new data, from which the algorithm learns:
A set of input and output pairs, where the input is the data to be analyzed.
The output is the correct answer or label associated with that input.
During the training process, the algorithm adjusts its internal parameters to map (establish a mathematical relationship) the input to the output. The goal of supervised learning is to create a model that can accurately predict the output of new and unknown input data.
There are two main types of supervised learning:
Regression.
Classification.
2. Classification in supervised machine learning:
A model is trained to predict a label or categorical class for a given input, based on a set of labeled examples. In other words, classification is the process of finding a function that maps input variables to discrete output variables.
Some common examples of classification tasks include identifying unwanted emails, predicting whether a customer will unsubscribe, classifying images into different categories, and identifying sentiment in text data.
There are several classification algorithms:
Logistic regression:
It is used to predict the probability that an observation belongs to one of two (binary) categories, such as "yes" or "no", "true" or "false", "sick" or "healthy", etc.
It models the relationship between the independent variables and the dependent variable, which is the outcome variable or the variable to be predicted.
This function produces an S-shaped curve that transforms the input values into a scale from 0 to 1, representing the probability that the observation falls into one of two categories.
The logistic regression model uses a set of coefficients (weights) to weight the independent variables and fit the logistic curve to the training data. These coefficients are estimated using a method called maximum likelihood, which seeks to maximize the probability that the training data fit the logistic curve.
Decision Trees:
It is a model that is built from a series of simple decisions based on characteristics or attributes of the input data:
The decision tree is composed of nodes and branches.
Each node represents a decision based on an attribute of the input data, and each branch represents an output based on that decision.
At the top of the tree is the root node, which represents the first decision in the tree.
As one moves down the tree, more decisions are made, leading to a final classification or value prediction.
Decision trees are useful because they are easy to understand and interpret and can handle numerical and categorical data.
Support Vector Machines (SVM):
They are a type of model used for classification and regression. Unlike decision trees, which are based on a series of simple decisions, SVMs attempt to find the hyperplane that best separates the input data into two or more classes.
In SVM, each data point is represented in an n-dimensional space, where n is the number of attributes of the input data.
SVM searches for the hyperplane that maximizes the distance between data points of different classes, called the margin, and uses this hyperplane to classify new data.
SVMs are useful because they can handle nonlinear data by using kernel functions that map the data to a higher dimensional space.
SVMs have diverse applications in the fields of computer vision, natural language processing, and bioinformatics, among others. However, training them on large and complex datasets can be computationally expensive.
Neural networks:
They are a type of model inspired by the functioning of the human brain.
They consist of layers of processing units called neurons, which are connected by synaptic weights.
Input data is fed into the input layer, which is connected to one or more hidden layers of neurons that process the data and transmit it to the output layer.
The connections between neurons are adjusted during network training to minimize a loss function, which measures the prediction error of the network.
Neural networks are useful because they can learn complex patterns in the input data and handle nonlinear data.
They can also handle high-dimensional data and can be used for classification, regression, and text and image generation tasks.
The choice of classification technique depends on the complexity of the problem, the size and quality of the data set and other factors.
GPT-chat
It is a language model developed by Open AI based on the GPT-3.5 architecture. It is capable of generating human-like responses to natural language questions and can be used for a wide range of applications, such as:
Applications in the financial industry:
Credit Risk Assessment: scoring can be used to classify borrowers as high or low risk based on their credit history and other relevant variables, such as income and employment status. This helps financial institutions make more informed credit decisions and manage credit risk.
Fraud detection: Classification can be used to classify transactions as fraudulent or legitimate based on patterns and anomalies in transaction data. This can help financial institutions detect and prevent fraud, such as credit card fraud or money laundering.
Customer segmentation: Classification can be used to segment customers into different groups based on their characteristics, such as age, income and spending habits. This can help financial institutions target their marketing efforts and offer more personalized services to customers.
Investment analysis: Ranking can be used to classify securities as buy, hold or sell based on a variety of factors, such as company performance metrics and economic indicators. This helps financial institutions make more informed investment decisions.
Big Data Applications:
Customer Churn Prediction: Classification can be used to classify customers as churn-prone or non-churn-prone based on their historical behavior, such as purchase frequency and customer service interactions. With big data, classification models can be trained on large data sets to provide more accurate predictions and insights.
Image and speech recognition: Classification can be used in natural language processing and machine vision applications for image and speech recognition. For example, classification can be used to classify images based on visual features or to classify speech based on phonemes.
Sentiment Analysis: Classification can be used to classify text data, such as customer reviews or social media posts, into positive or negative sentiment categories. With big data, classification models can be trained on large data sets to provide more accurate sentiment analysis and a better understanding of customer opinions and preferences.
Fraud detection: Classification can be used to classify transactions as fraudulent or legitimate based on patterns and anomalies in transaction data. With big data, classification models can be trained on large transaction data sets to provide more accurate fraud detection and prevention.
Applications in the real estate industry:
Property Classification: Classification can be used to sort properties into different categories based on characteristics such as location, size and amenities. This helps real estate professionals to target their marketing efforts and offer more personalized services to clients.
Buyer and Seller Classification: Classification can be used to sort buyers and sellers into different categories based on their characteristics such as age, income and purchase history. This helps real estate professionals provide more personalized services to customers and optimize their marketing efforts.
Lead Scoring: Classification can be used to score leads based on their likelihood of converting to a sale or listing. With big data, classification models can be trained on large data sets to provide more accurate lead scoring and a better understanding of customer behavior.
Price Range Classification: Classification can be used to sort properties into different price range categories based on factors such as location, size and amenities. This helps real estate professionals provide more accurate price recommendations to clients and optimize their pricing strategies.
Closing remark:
Overall, supervised machine learning classification techniques have a wide range of applications across all industry sectors, enabling professionals to offer more personalized services to customers and optimize their marketing and pricing strategies.