Large language models like ChatGPT and Bard have raised machine learning to the status of a phenomenon. Their use for coding assistance has quickly earned these tools a place in the developer’s toolkit. Other use cases are being explored, ranging from image generation to disease detection.
Tech companies are investing heavily in machine learning, so knowing how to train and work with models is becoming essential for developers.
This article gets you started with machine learning in Java. You will get a first look at how machine learning works, followed by a short guide to implementing and training a machine learning algorithm. We’ll focus on supervised machine learning, which is the most common approach to developing intelligent applications.
Machine learning and AI
Machine learning has evolved from the field of artificial intelligence (AI), which seeks to produce machines capable of mimicking human intelligence. Although machine learning is a hot trend in computer science, AI is not a new field in science. The Turing test, developed by Alan Turing in the early 1950s, was one of the first tests created to determine whether a computer could have real intelligence. According to the Turing test, a computer could prove human intelligence by tricking a human into believing it was also human.
Many state-of-the-art machine learning approaches are based on decades-old concepts. What has changed over the past decade is that computers (and distributed computing platforms) now have the processing power required for machine learning algorithms. Most machine learning algorithms demand a huge number of matrix multiplications and other mathematical operations to process. The computational technology to manage these calculations didn’t exist even two decades ago, but it does today. Parallel processing and dedicated chips, as well as big data, have radically increased the capacity of machine learning platforms.
Machine learning enables programs to execute quality improvement processes and extend their capabilities without human involvement. Some programs built with machine learning are even capable of updating or extending their own code.
How machines learn
Supervised learning and unsupervised learning are the most popular approaches to machine learning. Both require feeding the machine a massive number of data records to correlate and learn from. Such collected data records are commonly known as a feature vectors. In the case of an individual house, a feature vector might consist of features such as overall house size, number of rooms, and the age of the house.
Supervised learning
In supervised learning, a machine learning algorithm is trained to correctly respond to questions related to feature vectors. To train an algorithm, the machine is fed a set of feature vectors and an associated label. Labels are typically provided by a human annotator and represent the right answer to a given question. The learning algorithm analyzes feature vectors and their correct labels to find internal structures and relationships between them. Thus, the machine learns to correctly respond to queries.
As an example, an intelligent real estate application might be trained with feature vectors including the respective size, number of rooms, and age of a range of houses. A human labeller would label each house with the correct house price based on these factors. By analyzing the data, the real estate application would be trained to answer the question, “How much money could I get for this house?”
After the training process is over, new input data is not labeled. The machine is able to correctly respond to new queries, even for unseen, unlabeled feature vectors.
Unsupervised learning
In unsupervised learning, the algorithm is programmed to predict answers without human labeling, or even questions. Rather than predetermine labels or what the results should be, unsupervised learning harnesses massive data sets and processing power to discover previously unknown correlations. In consumer product marketing, for instance, unsupervised learning could be used to identify hidden relationships or consumer grouping, eventually leading to new or improved marketing strategies.
This article focuses on supervised machine learning, which is currently the most common approach to machine learning.
A supervised machine learning project
Now let’s look at an example: a supervised learning project for a real estate application.
All machine learning is based on data. Essentially, you input many instances of data and the real-world outcomes of that data, and the algorithm forms a mathematical model based on those inputs. The machine eventually learns to use new data to predict unknown outcomes.
For a supervised machine learning project, you will need to label the data in a meaningful way for the outcome you are seeking. In Table 1, note that each row of the house record includes a label for “house price.” By correlating row data to the house price label, the algorithm will eventually be able to predict market price for a house not in its data set (note that house size is based on square meters, and house price is based on euros).
Table 1. House records
| FEATURE | FEATURE | FEATURE | LABEL |
| Size of House | Number of Rooms | Age of House | Estimated Cost |
|
90 m2 / 295 ft |
2 | 23 years |
249,000 € |
|
101 m2 / 331 ft |
3 | N/A |
338,000 € |
|
1330 m2 / 4363 ft |
11 | 12 years |
6,500,000 € |
In the early stages, you will likely label the data records by hand, but you could eventually train your program to automate this process. You’ve probably seen this with email applications, where moving email into your spam folder results in the query “Is this spam?” When you respond, you are training the program to recognize mail that you don’t want to see. The application’s spam filter learns to label and dispose of future mail from the same source or containing similar content.
Labeled data sets are only required for training and testing purposes. After this phase is over, the machine learning model works on unlabeled data instances. For instance, you could feed the prediction algorithm a new, unlabeled house record and it would automatically predict the expected house price based on training data.
Training a machine learning model
The challenge of supervised machine learning is to find the proper prediction function for a specific question. Mathematically, the challenge is to find the input/output function that takes the input variable x and returns the prediction value y. This hypothesis function (hθ) is the output of the training process. Often, the hypothesis function is also called target or prediction function.
Figure 1. Example of a target function
In most cases, x represents a multiple-data point. In our example, this could be a two-dimensional data point of an individual house defined by the house-size value and the number-of-rooms value. The array of these values is referred to as the feature vector. Given a concrete target function, the function can be used to make a prediction for each feature vector, x. To predict the price of an individual house, you could call the target function by using the feature vector { 101.0, 3.0 }, which contains the house size and the number of rooms:
Listing 1. Calling the target function with a feature vector
// target function h (which is the output of the learn process)
Function<Double[], Double> h = ...;
// set the feature vector with house size=101 and number-of-rooms=3
Double[] x = new Double[] { 101.0, 3.0 };
// and predicted the house price (label)
double y = h.apply(x);
In Listing 1, the array variable x value represents the feature vector of the house. The y value returned by the target function is the predicted house price.
The challenge of machine learning is to define a target function that will work as accurately as possible for unknown, unseen data instances. In machine learning, the target function (hθ) is sometimes called a model. This model is the result of the learning process, also called model training.
Figure 2. A machine learning model
Based on labeled training examples, the learning algorithm looks for structures or patterns in the training data. It does this with a process known as back propagation, where values are gradually modified to reduce loss. From these, it produces a model that is able to generalize from that data.
Typically, the learning process is explorative. In most cases, the process will be executed multiple times using different variations of learning algorithms and configurations. When a model is settled upon, the data is run through it many times as well. These iterations are known as epochs.
Eventually, all the models will be evaluated based on performance metrics. The best one will be selected and used to compute predictions for future unlabeled data instances.
Linear regression
To train a machine to think, the first step is to choose the learning algorithm you’ll use. Linear regression is one of the simplest and most popular supervised learning algorithms. This algorithm assumes that the relationship between input features and the output label is linear. The generic linear regression function in Figure 3 returns the predicted value by summarizing each element of the feature vector multiplied by a theta parameter (θ). The theta parameters are used within the training process to adapt or “tune” the regression function based on the training data.
Figure 3. A generic linear regression function
Linear regression is a simple kind of learning function, but it provides a good basis for the more advanced forms like gradient descent, which is used in feed forward neural networks. In the linear regression function, theta parameters and feature parameters are enumerated by a subscription number. The subscription number indicates the position of theta parameters (θ) and feature parameters (x) within the vector. Note that feature x0 is a constant offset term set with the value 1 for computational purposes. As a result, the index of a domain-specific feature such as house-size will start with x1. So, if x1 is set for the first value of the House feature vector, house-size, then x2 will be set for the next value, number-of-rooms, and so forth.
Listing 2 shows a Java implementation of this linear regression function, shown mathematically as hθ(x). For simplicity, the calculation is done using the data type double. Within the apply() method, it is expected that the first element of the array has been set with a value of 1.0 outside of this function.
Listing 2. Linear regression in Java
public class LinearRegressionFunction implements Function<Double[], Double> {
private final double[] thetaVector;
LinearRegressionFunction(double[] thetaVector) {
this.thetaVector = Arrays.copyOf(thetaVector, thetaVector.length);
}
public Double apply(Double[] featureVector) {
// for computational reasons the first element has to be 1.0
assert featureVector[0] == 1.0;
// simple, sequential implementation
double prediction = 0;
for (int j = 0; j < thetaVector.length; j++) {
prediction += thetaVector[j] * featureVector[j];
}
return prediction;
}
public double[] getThetas() {
return Arrays.copyOf(thetaVector, thetaVector.length);
}
}
In order to create a new instance of the LinearRegressionFunction, you must set the theta parameter. The theta parameter, or vector, is used to adapt the generic regression function to the underlying training data. The program’s theta parameters will be tuned during the learning process, based on training examples. The quality of the trained target function can only be as good as the quality of the given training data.
In the next example, the LinearRegressionFunction will be instantiated to predict the house price based on house size. Considering that x0 has to be a constant value of 1.0, the target function is instantiated using two theta parameters. The theta parameters are the output of a learning process. After creating the new instance, the price of a house with size of 1330 square meters will be predicted as follows:
// the theta vector used here was output of a train process
double[] thetaVector = new double[] { 1.004579, 5.286822 };
LinearRegressionFunction targetFunction = new LinearRegressionFunction(thetaVector);
// create the feature vector function with x0=1 (for computational reasons) and x1=house-size
Double[] featureVector = new Double[] { 1.0, 1330.0 };
// make the prediction
double predictedPrice = targetFunction.apply(featureVector);
The target function’s prediction line is shown as a blue line in Figure 4. The line has been computed by executing the target function for all the house-size values. The chart also includes the price-size pairs used for training.
Figure 4. The target function’s prediction line
So far, the prediction graph seems to fit well enough. The graph coordinates (the intercept and slope) are defined by the theta vector { 1.004579, 5.286822 }. But how do you know that this theta vector is the best fit for your application? Would the function fit better if you changed the first or second theta parameter? To identify the best-fitting theta parameter vector, you need a utility function, which will evaluate how well the target function performs.
Scoring the target function
In machine learning, a cost function (J(θ)) (aka “loss function”) is used to compute the mean error, or “cost” of a given target function. Figure 5 shows an example.
Figure 5. A cost function
The cost function indicates how well the model fits with the training data. To determine the cost of our trained target function, you would compute the squared error of each house example (i). The error is the distance between the calculated y value and the real y value of a house example i.
For instance, the real price of the house with a size of 1330 is 6,500,000 €. In contrast, the predicted house price of the trained target function is 7,032,478 €, a gap (or error) of 532,478 €. You can also find this gap in the chart shown in Figure 4. The gap (or error) is shown as a vertical dotted red line for each training price-size pair.
To compute the cost of the trained target function, you must summarize the squared error for each house in the example and calculate the mean value. The smaller the cost value of J(θ), the more precise the target function’s predictions will be.
In the listing below, the simple Java implementation of the cost function takes as input the target function, the list of training records, and their associated labels. The predicted value will be computed in a loop, and the error will be calculated by subtracting the real label value.
Afterward, the squared error will be summarized and the mean error will be calculated. The cost will be returned as a double value:
public static double cost(Function<Double[], Double> targetFunction,
List<Double[]> dataset,
List<Double> labels) {
int m = dataset.size();
double sumSquaredErrors = 0;
// calculate the squared error ("gap") for each training example and add it to the total sum
for (int i = 0; i < m; i++) {
// get the feature vector of the current example
Double[] featureVector = dataset.get(i);
// predict the value and compute the error based on the real value (label)
double predicted = targetFunction.apply(featureVector);
double label = labels.get(i);
double gap = predicted - label;
sumSquaredErrors += Math.pow(gap, 2);
}
// calculate and return the mean value of the errors (the smaller the better)
return (1.0 / (2 * m)) * sumSquaredErrors;
}
Training the target function with gradient descent
Although the cost function helps to evaluate the quality of the target function and theta parameters, respectively, you still need to compute the best-fitting theta parameters. You can use the gradient descent algorithm for this calculation.
Computing theta parameters with gradient descent
Gradient descent minimizes the cost function, meaning that it’s used to find the theta combinations that produces the lowest cost (J(θ)) based on the training data. Gradient descent does this by gradually adjusting each variable using partial derivatives. This is the archetypal form of back propagation, upon which all others are based.
Figure 6 shows a simplified algorithm to compute new, better fitting thetas:
Figure 6. Gradient descent minimizes the cost function
Within each iteration a new, better value will be computed for each individual θ parameter of the theta vector. The learning rate α controls the size of the computing step within each iteration. This computation will be repeated until you reach a theta values combination that fits well. As an example, the linear regression function in Figure 7 has three theta parameters:
Figure 7. A linear regression function with three theta parameters
Within each iteration (“epoch”) a new value will be computed for each theta parameter: θ0, θ1, and θ2 in parallel. After each iteration, you will be able to create a new, better-fitting instance of the LinearRegressionFunction by using the new theta vector of {θ0, θ1, θ2}.
Listing 3 shows Java code for the gradient descent algorithm. The thetas of the regression function will be trained using the training data, data labels, and the learning rate (α). The output of the function is an improved target function using the new theta parameters. The train() method will be called over and over, and fed the new target function along with the new thetas from the previous calculation. These calls will repeat until the tuned target function’s cost reaches a minimal plateau.
Listing 3. Example of a gradient descent algorithm in Java
public static LinearRegressionFunction train(LinearRegressionFunction targetFunction,
List<Double[]> dataset,
List<Double> labels,
double alpha) {
int m = dataset.size();
double[] thetaVector = targetFunction.getThetas();
double[] newThetaVector = new double[thetaVector.length];
// compute the new theta of each element of the theta array
for (int j = 0; j < thetaVector.length; j++) {
// summarize the error gap * feature
double sumErrors = 0;
for (int i = 0; i < m; i++) {
Double[] featureVector = dataset.get(i);
double error = targetFunction.apply(featureVector) - labels.get(i);
sumErrors += error * featureVector[j];
}
// compute the new theta value
double gradient = (1.0 / m) * sumErrors;
newThetaVector[j] = thetaVector[j] - alpha * gradient;
}
return new LinearRegressionFunction(newThetaVector);
}
To validate that the cost decreases continuously, you can execute the cost function J(θ) after each training step. With each iteration, the cost must decrease. If it doesn’t, then the value of the learning rate parameter is too large, and the algorithm will shoot past the minimum value. In this case, the gradient descent algorithm fails.
Why the model doesn’t work
The diagram in Figure 8 shows the target function using the computed, new theta parameter, starting with an initial theta vector of { 1.0, 1.0 }. The left-side column shows the prediction graph after 50 iterations; the middle column after 200 iterations; and the right column after 1,000 iterations. As you see, the cost decreases after each iteration, as the new target function fits better and better. After 500 to 600 iterations the theta parameters no longer change significantly and the cost reaches a stable plateau. The accuracy of the target function will no longer significantly improve from this point.
Figure 8. Cost decreases with each iteration
In this case, although the cost will no longer decrease significantly after 500 to 600 iterations, the target function is still not optimal; it seems to underfit. In machine learning, the term underfitting is used to indicate that the learning algorithm does not capture the underlying trend of the data.
Based on real-world experience, it is expected that the price per square metre will decrease for larger properties. From this we conclude that the model used for the training process, the target function, does not fit the data well enough. Underfitting is often due to an excessively simple model. In this case, it’s the result of our simple target function using a single house-size feature only. That data alone is not enough to accurately predict the cost of a house.
Adding features and feature scaling
If you discover that your target function doesn’t fit the problem you are trying to solve, you can adjust it. A common way to correct underfitting is to add more features into the feature vector.
In the housing-price example, you could add more house characteristics such as the number of rooms or the age of the house. Rather than using the single domain-specific feature vector of { size } to describe a house instance, you could use a multi-valued feature vector such as { size, number-of-rooms, age }.
In some cases, there aren’t enough features in the available training data set. In this case, you can try adding polynomial features, which are computed by existing features. For instance, you could extend the house-price target function to include a computed squared-size feature (x2):
Figure 9. Target function extended with polynomial features
Using multiple features requires feature scaling (or “normalization”), which is used to standardize the range of different features. For instance, the value range of the size2 feature is a magnitude larger than the range of the size feature. Without feature scaling, the size2 feature will dominate the cost function. The error value produced by the size2 feature will be much higher than the error value produced by the size feature alone. A simple algorithm for feature scaling is shown in Figure 10.
Figure 10. A simple feature-scaling algorithm
This algorithm is implemented by the FeaturesScaling class in the example Java code below. The FeaturesScaling class provides a factory method to create a scaling function adjusted on the training data. Internally, instances of the training data are used to compute the average, minimum, and maximum constants. The resulting function consumes a feature vector and produces a new one with scaled features. The feature scaling is required for the training process, as well as for the prediction call, as shown below:
// create the dataset
List<Double[]> dataset = new ArrayList<>();
dataset.add(new Double[] { 1.0, 90.0, 8100.0 }); // feature vector of house#1
dataset.add(new Double[] { 1.0, 101.0, 10201.0 }); // feature vector of house#2
dataset.add(new Double[] { 1.0, 103.0, 10609.0 }); // ...
//...
// create the labels
List<Double> labels = new ArrayList<>();
labels.add(249.0); // price label of house#1
labels.add(338.0); // price label of house#2
labels.add(304.0); // ...
//...
// scale the extended feature list
Function<Double[], Double[]> scalingFunc = FeaturesScaling.createFunction(dataset);
List<Double[]> scaledDataset = dataset.stream().map(scalingFunc).collect(Collectors.toList());
// create hypothesis function with initial thetas and train it with learning rate 0.1
LinearRegressionFunction targetFunction = new LinearRegressionFunction(new double[] { 1.0, 1.0, 1.0 });
for (int i = 0; i < 10000; i++) {
targetFunction = Learner.train(targetFunction, scaledDataset, labels, 0.1);
}
// make a prediction of a house with size if 600 m2
Double[] scaledFeatureVector = scalingFunc.apply(new Double[] { 1.0, 600.0, 360000.0 });
double predictedPrice = targetFunction.apply(scaledFeatureVector);
As you add more features, you may find that the target function fits better and better—but beware! If you go too far, and add too many features, you could end up with a target function that is overfitting.
Overfitting and cross-validation
Overfitting occurs when the target function or model fits the training data too well. A model that is overfitting captures noise or random fluctuations in the training data. The far-right side of the graph in Figure 11 shows a pattern of overfitting behavior.
Figure 11. Example of a target function with overfitting behavior
Although an overfitting model matches very well on the training data, it will perform badly when asked to solve for unknown, unseen data. There are a few ways to avoid overfitting:
- Use a larger set of training data.
- Improve the machine learning algorithm with the addition of regularization.
- Use fewer features, as shown in the middle diagram above.
If your predictive model overfits, you should remove any features that do not contribute to its accuracy. The challenge here is to find the features that contribute most meaningfully to your prediction output.
As shown in the diagrams, overfitting can be identified by visualizing graphs. Even though this works well using two-dimensional or three-dimensional graphs, it will become difficult if you use more than two domain-specific features. This is why cross-validation is often used to detect overfitting.
In a cross-validation, you evaluate the trained models using an unseen validation data set after the learning process has completed. The available, labeled data set will be split into three parts:
- The training data set.
- The validation data set.
- The test data set.
In this case, 60 percent of the house example records may be used to train different variants of the target algorithm. After the learning process, half of the remaining, untouched example records will be used to validate that the trained target algorithms work well for unseen data.
Typically, the best-fitting target algorithms will then be selected. The other half of untouched example data will be used to calculate error metrics for the final, selected model. While I won’t introduce them here, there are other variations of this technique, such as k fold cross-validation.
Conclusion to Part 1
In this article, you’ve had a chance to get started with machine learning in Java. We’ve introduced a supervised learning example and used the gradient descent algorithm to train a target function. You’ve also seen an example of an underfitting model and how to correct it by adding features and feature scaling. We’ve also briefly discussed the dangers of overfitting and how to correct them.
The second half of this article will continue the focus on Java-based machine learning. We’ll introduce Weka, a JVM-based machine learning framework, then get to work developing a machine learning data model in a Java-based environment.
