When it comes to today’s machine learning algorithms, they are being used in a variety of ways as well as in different fields. Healthcare, businesses, research are just a few examples where artificial intelligence is being applied to solve problems that either humans could not do by themselves or would take massive amounts of time to solve.
However, everything in life has its strengths and weaknesses; modern machine learning algorithms are unfortunately no exception to this rule. Earlier this year, an article posted on the Elite Data Science website focused on several types of today’s modern machine learning algorithms as well as their strengths and weakness.
Basically, there is no super algorithm that can solve every problem and that it would be an innovative idea to try a variety of algorithms to resolve the problem at hand. The following are several examples of machine learning tasks and what are the strengths and weaknesses for each of them.
Regression Machine Learning Task
Regression is known as the supervised learning task for predicting and modeling continuous, numeric variables; examples of this includes predicting student test scores, real-estate prices and stock price movements. While linear regression is an algorithm that is most common for the regression task, another form is known as a regression tree (a.k.a. decision trees). When it comes to decision tree learning, it utilizes a decision tree, as a predictive model, to start from using observations regarding an item (pictured in the branches) to finishing with conclusions regarding the target value of the item; these are represented in the leaves.
Regression trees acquire their knowledge in a hierarchical fashion because it repeatedly splits the dataset into separate branches; this maximizes the information acquired with each split. The strength of using this is that decision trees can understand non-linear relationships and can be robust to outliers; however, a weakness to this algorithm is that it is unconstrained and that a tree can be prone to overfitting as it can keep branching out until it memorizes the training data.
Classification Machine Learning Task
When it comes to classification, this is a supervised learning task for predicting and modeling categorical variables; examples of this includes predicting student letter grades, employee churn, financial fraud and email spam. Referring to classification trees (ensembles), they are the classification counterparts to the previously mentioned regression trees.
They commonly both associated with decision trees or by the umbrella term known as Classification and Regression Trees (CART). Just like with regression, classification trees ensembles in practice also performs very well, however, they can also be unconstrained as a tree can become prone to overfitting as well.
Considerations When Picking an Algorithm to Use
When deciding on what type of algorithm you want to use, there are variables that need to be considered as well as what you hope to accomplish. When considerations are ignored in picking an algorithm to use, it can bring about a situation where the wrong type of algorithm is used and cause more problems than what you started with.
Accuracy is important to think about as there are times that an approximation is good enough; this results in saving resources instead of wasting them. Another thing to consider has to do with the training time associated with the algorithm; large sets of data accumulation require more time and resources to resolve instead of smaller sets of information. One other thing to consider has to do with linearity, which many machine learning algorithms make use of; this classification of algorithms presumes that classes can be isolated through the use of a straight line.
Artificial intelligence. Machine learning and algorithms all signify that humans either cannot solve a problem by themselves or it would take too long to solve on their own. While algorithms continue to evolve depending on what problem needs to be resolved, there is no super-algorithm that can resolve every situation. Therefore, it is s important to understand the strengths and weaknesses of Modern Machine Learning Algorithms to solve problems today and especially those of the future.