Machine Learning Approaches | Types
There are many approaches that can be taken when conducting Machine Learning. They are usually grouped into the areas listed below. Supervised and Unsupervised are well established approaches and the most commonly used. Semi-supervised and Reinforcement Learning are newer and more complex but have shown impressive results.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning:
In supervised learning, the goal is to learn the mapping (the rules) between a set of inputs and outputs.
For example, the inputs could be the weather forecast, and the outputs would be the visitors to the beach. The goal in supervised learning would be to learn the mapping that describes the relationship between temperature and number of beach visitors.
Example labelled data is provided of past input and output pairs during the learning process to teach the model how it should behave, hence, ‘supervised’ learning. For the beach example, new inputs can then be fed in of forecast temperature and the Machine learning algorithm will then output a future prediction for the number of visitors.
Supervised learning is of two types:
1. Regression
2. Classification
Classification:
Classification is used to group the similar data points into different sections in order to classify them. Machine Learning is used to find the rules that explain how to separate the different data points.
In simple words, classification approaches try to find the best way to separate data points with a line.
The lines drawn between classes are known as the decision boundaries. The entire area that is chosen to define a class is known as the decision surface. The decision surface defines that if a data point falls within its boundaries, it will be assigned a certain class.
7 Types of Classification Algorithms
- Logistic Regression.
- Naïve Bayes.
- Stochastic Gradient Descent.
- K-Nearest Neighbours.
- Decision Tree.
- Random Forest.
- Support Vector Machine.
Some examples of classification include spam detection, sentiment analysis, dog breed detection and so on.
Regression:
Regression is another form of supervised learning. The difference between classification and regression is that regression outputs a number rather than a class.
List of regression algorithms in Machine Learning
- Linear Regression.
- Ridge Regression.
- Neural Network Regression.
- Lasso Regression.
- Decision Tree Regression.
- Random Forest.
- KNN Model.
- Support Vector Machines (SVM)
Therefore, regression is useful when predicting number based problems like stock market prices, the temperature for a given day, or the probability of an event.
Unsupervised Learning:
In unsupervised learning, only input data is provided in the examples. There are no labelled.
An example of unsupervised learning in real life would be sorting different colour coins into separate piles. Nobody taught you how to separate them, but by just looking at their features such as colour, you can see which colour coins are associated and cluster them into their correct groups.
To find the interesting structures in un-labelled data, we use density estimation. The most common form of which is clustering. Among others, there is also dimensionality reduction, latent variable models and anomaly detection.
Clustering:
Unsupervised learning is mostly used for clustering. Clustering is the act of creating groups with differing characteristics. Clustering attempts to find various subgroups within a dataset. As this is unsupervised learning, we are not restricted to any set of labels and are free to choose how many clusters to create.
Some types of clustering:
- K-Means
- Mini-Batch K-Means
- Mean Shift
Association:
In Association Learning you want to uncover the rules that describe your data. For example, if a person watches video A they will likely watch video B. Association rules are perfect for examples such as this where you want to find related items.
Anomaly Detection:
The identification of rare or unusual items that differ from the majority of data. For example, your bank will use this to detect fraudulent activity on your card. Your normal spending habits will fall within a normal range of behaviours and values. But when someone tries to steal from you using your card the behaviour will be different from your normal pattern. Anomaly detection uses unsupervised learning to separate and detect these strange occurrences.
Dimensionality Reduction
Dimensionality reduction aims to find the most important features to reduce the original feature set down into a smaller more efficient set that still encodes the important data.
For example, in predicting the number of visitors to the beach we might use the temperature, day of the week, month and number of events scheduled for that day as inputs. But the month might actually be not important for predicting the number of visitors.
Reinforcement Learning
The final type of machine learning, it is less common and much more complex, but it has generated incredible results. It doesn’t use labels as such, and instead uses rewards to learn.
This is very similar to how we as humans also learn. Throughout our lives, we receive positive and negative signals and constantly learn from them. The chemicals in our brain are one of many ways we get these signals.
When something good happens, the neurons in our brains provide a hit of positive neurotransmitters such as dopamine which makes us feel good and we become more likely to repeat that specific action. We don’t need constant supervision to learn like in supervised learning. By only giving the occasional reinforcement signals, we still learn very effectively.
In this part I have covered almost all the types and sub types of machine learning one need to remember in a very brief way with real life examples hope it will be helpful, Thank you for reading this far!