Artificial Neural Network Model Classification and Regression
Unlike human beings who often learn for the intrinsic value of knowing something, machine-learning is almost always purpose-driven. Your job as the machine's developer is to determine what that purpose is before you start development. With neural network regression and classification models, you then need to decide on the methodology that will best serve that purpose. Ask yourself, “Am I looking at a classification problem, a regression problem, or a clustering problem?” Those are the three things artificial neural networks do best: classification, regression, and clustering. Here’s how you choose:
1. Classification?is best when you need to assign inputs to known (labeled) categories. There are two types of classification:
2.?Regression?is best when you need to predict a?continuous response value in regression analysis?— a variable that can take on any value between its minimum and maximum value; for example, if you need a system that can predict the value of a home based on certain criteria, such as square footage, location, number of bedrooms and bathrooms, and so on.
3.?Clustering?is the right choice when you want to identify patterns in the data and have no idea what those patterns may be; for example, if you want to identify patterns among loyal, somewhat loyal, and un-loyal customers.
Supervised Versus Unsupervised Learning: Predictive Modeling
Classification and regression problems involve?supervised?learning — using training data, to teach the machine (the artificial neural network with hidden and output layers) how to associate inputs with outputs. For example, you may feed the machine a picture of a cat and tell it, "This is a cat." You feed it a picture of a dog and tell it, "This is a dog." Then, you feed the machine test data; for example, a picture of a cat without telling the machine what the animal in the picture is, and the machine should be able to tell you it's a cat. If the machine gives the incorrect answer, you correct it, and the machine, through its hidden layer, makes adjustments to improve its prediction accuracy.
Clustering problems are in the realm of?unsupervised?learning. You feed the machine data inputs without labels, and the machine identifies common patterns among the inputs without labeling those patterns.?
Solving Classification and Regression Problems
Classification is one of the most common ways to use an artificial neural network for categorical data processing. For example, credit card companies use classification to detect and prevent fraudulent transactions. The human trainer will feed the machine an example of a fraudulent transaction and tell the machine, "This is fraud." The trainer then feeds the machine an example of an honest transaction and tells the machine, "This is not fraud." As the trainer feeds more and more labeled data into the machine, it learns the patterns in the dataset that distinguish fraudulent transactions from honest transactions through regression analysis.
The machine may be set up with three output nodes (one for each class). If a transaction is highly characteristic of fraud, the Fraud neuron in the output layer fires to cancel the transaction and suspend the card. If a transaction is less characteristic of fraud, the Maybe Fraud neuron fires to notify the cardholder of suspicious activity. If the transaction is even less characteristic of fraud, the Not Fraud neuron in the output layer fires and the transaction is processed.?
Solving Regression Problems: Regression Model
In regression problems, the machine tries to come up with an approximation based on the dataset input. During the training session, instead of showing the machine how inputs are connected to labels, you show the machine the connection between a known outcome and the variables that impact that outcome. For example, the amount of time it takes to drive home from work varies depending on weather conditions, traffic conditions, and the time of day, as shown below.
A stock price predictor would be another example of machine learning used to solve a regression problem, typically employing a regression model for prediction. The stock price would be the dependent variable and would be driven by a host of independent variables, including earnings, profits, future estimated earnings, a change of management, accounting errors or scandals, and so forth.
One way to look at the difference between classification and regression is that with classification the output requires a class label, whereas with regression the output is an approximation or likelihood.
In my next newsletter, I examine an entirely different type of problem — those that can be solved not by classification or regression but by clustering.
Frequently Asked Questions
What are the basic principles behind artificial neural network classification models?
Neural networks can spot patterns. They do this by learning from data.
First, you train a neural network model using a dataset to evaluate its performance. This dataset, containing known answers, allows for the neural network to predict outcomes with higher accuracy. The network learns by adjusting its inside settings. It uses a method called stochastic gradient descent. This method helps it guess better by lowering the mistakes. These mistakes are measured by a metric called a loss function, like cross-entropy, in tasks where you need to sort things into categories.
Think of the process like teaching a child to recognize animals. At first, the child makes mistakes, but with guidance, they learn to get it right.
1. Neural networks learn from data, adjusting their parameters each time. 2. They use a method that helps them improve their predictions by fixing mistakes. 3. This learning helps them sort and classify complex patterns correctly, making nn technologies crucial for processing categorical data.
The end goal of this is that the neural network becomes smarter and can make accurate classifications.
How does a perceptron work in the context of artificial neural networks?
A perceptron is a simple type of machine that sorts things into two groups. It is the easiest kind of neural network. It helps in tasks where you need to decide between two choices. Here's how it works:
- It takes features (small parts of the input). - It multiplies them by weights (numbers that show importance). - It adds them up along with a bias term (another number).
Then, it uses an activation function (a rule to decide the final output). If this output is above a certain level, the perceptron puts the input into one group. If it is below, the input goes into another group.
Perceptrons are simple but powerful. They set the stage for more complex neural networks.
Can artificial neural networks be used for linear regression as well as classification in data science?
Yes, you can use neural networks for both predicting numbers and sorting items into groups. Here's how:
1. For predicting numbers, the network learns to predict continuous outcomes. 2. It adjusts its weights based on the difference between its predictions and the actual numbers. 3. It often uses mean square error, which measures these differences, to improve. 4. For sorting items, the final layer uses a softmax activation to place inputs into different categories.
What role does the activation function play in neural networks?
The activation function in a neural network is very important. It adds non-linearity to the network's work.
This helps the network learn and understand complex links between input and output data.
If there were no non-linear activation functions, the neural network would act like a simple linear tool.
This would make it hard to classify complex patterns and predict non-linear data. Some common activation functions are Rectified Linear Unit, sigmoid, and tanh.
- Neural networks need non-linearity to learn. - Linear networks can't handle complex patterns. - Rectified Linear Unit helps with non-linear tasks. - Sigmoid is another activation function. - Tanh also adds non-linearity, a key parameter in the efficacy of nn models.
What is the significance of multi-layer perceptrons (MLP) in informatics and data science?
Multi-layer perceptrons are a type of neural network. They are important in data science. They can model complex relationships between inputs and outputs. They use layers of neurons with non-linear activation functions, exemplifying how MLP neural networks operate.
Multi-layer perceptrons can do many tasks well: - Recognize images - Process natural language - Classify medical data - Make accurate predictions
They are versatile and powerful. They help solve many prediction problems.
How is validation performed in the context of neural network models?
Validating a neural network means checking how well it works. You use a separate part of the data that you didn't use to teach the model, a method to evaluate how well the neural network predicts unseen data. This helps you see if the model is only good with the training data or if it can handle new data too. If the model does well with training data but not with new data, this is called overfitting.
One common way to validate is to use k-fold cross-validation. You split the data into k parts. You train and test the model k times, each time using a different part as the test data.
You can check how good the model is with different measures: - For sorting tasks, use accuracy. - For predicting numbers, use mean square error.
In short, validating helps confirm the model works well not only on known data but also on new data.
This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or LLMs. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and ethics.
This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).
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"?? Servant Leader | Sr. Tech & DevOps Maestro ?? | Ethical AI/ML Architect ?? | Digital Transformation"
4 小时前Great read so far - wanted to hop in the comments couldn’t contain my enthusiasm. Would you say that clustering could be seen and used as a way to side load taxonomy data? Especially when that taxonomy and it’s classification (dimensions) aren’t known or easily predicted. I see great value in that method for ambiguous use cases. You take the data in managed/controled groups and increments, which for me is analogous to batch processing. Now imagine when we more or are larger datasets have an option to easily integrate with a continues data source like a Datalake. The exponentially impact for that is going to be insane!
?? Finance & Accounting Leader | PMO & Process Optimization | AI & Automation Enthusiast | ?? Global Finance & Strategy | ?? Open to Networking & Opportunities
16 小时前Very helpful
Sr IT Project Manager
18 小时前Interesting article with great analogies. Love that it is marked "Human Written".