There is a big difference between a traditional algorithm and a machine learning algorithm (ML). Traditional algorithms are designed for problems which we already know how to solve and so we explicitly program the computer to solve them. In contrast, machine learning algorithms deal with problems without clear-cut steps towards a solution.
In machine learning, no one algorithm works best for every problem, and it’s especially relevant for supervised learning (i.e. predictive modeling). For example, we can not say neural networks are always better than decision trees or vice-versa. There are many factors which must be considered such as the size and structure of your dataset.
As a result, we should try many different algorithms for our problem, while using a hold-out ‘test’ dataset to evaluate performance and select the winner.
A machine learning (ML) algorithm is essentially a process or sets of procedures that helps a model adapt to the data given an objective. An ML algorithm normally specifies the way the data is transformed from input to output and how the model learns the appropriate mapping from input to output.
We can say that: ML algorithm = model + learning algorithm.
Machine learning algorithms are a generic set of algorithms. They are not dictated by specific set of problem. The solution to specific problems lies in the DATA. Algorithms are trained with the dataset, and that results into specific problem model. These models are then used for the purpose of predicting the outcomes and solving specific cases/unknowns.
There is a common principle that underlies all supervised machine learning algorithms for predictive modeling.
Supervised Learning
Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X).
This is a general learning task where we would like to make predictions in the future (Y) given new examples of input variables (X). We don’t know what the function (f) looks like or its form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms.
The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and our goal is to make the most accurate predictions possible.
Unsupervised Learning
In unsupervised learning we have less information about objects and the train set is unlabeled. It’s possible to observe some similarities between groups of objects and include them in appropriate clusters. Some objects can differ hugely from all clusters, in this way we assume these objects to be anomalies.
Reinforcement Learning
Reinforcement Learning (RL) is not like any of our previous tasks because we don’t have labeled or unlabeled datasets here. RL is an area of machine learning concerned with how software agents ought to take actions in some environment to maximize some notion of cumulative reward.
