Association algorithms are unsupervised algorithms used to discover the probability of some items to occur together in a specific dataset. It is mostly used in the market-basket analysis.
Stacking: From the name, stacking has two-level stacked on top of each other, the base level is a combination of algorithms, and the top level is a meta-algorithm based on the base level results.
Regardless of the algorithm, it can generally be categorized as one of four categories: supervised, unsupervised, semi-supervised, and reinforced algorithms. Each one of these categories holds many algorithms that are used for different purposes.
Bagging: In bagging, the algorithms are run in parallel on different training sets, all equal in size. All algorithms are then tested using the same dataset, and voting is used to determine the overall results.
Machine learning is one of the most famous, well-researched sub-field of data science. New machine learning algorithms are always under development to reach better accuracy and faster execution.
In this article, I have gone through 5 types of supervised/ unsupervised algorithms that every machine learning beginner should be familiar with. These algorithms are well-studied and widely-used that you only need to understand how to use it rather than how to implement it.
Distribution-based Clustering: This clustering algorithm assumes the data is composed of probability distributions and then clusters the data into various versions of that distribution.
Polynomial Regression: This type of algorithm is used to fit non-linear data. Using it, the best prediction is not a straight line; it is a curve that tries to fit all data points.
The Apriori algorithm is a mining algorithm used commonly used in transactional databases. Apriori is used to mine frequent itemsets and generate some association rules from those item sets.
Centroid-based Clustering: This clustering algorithm organizes the data into clusters based on initial conditions and outliers. k-means is the most knowledgeable and used centroid-based clustering algorithm.
Clustering algorithms are a group of unsupervised algorithms used to group data points. Points within the same cluster are more similar to each other than to points in different clusters.
Classification in machine learning is the process of grouping items into categories based on a pre-categorized training dataset. Classification is considered a supervised learning algorithm.
For example, if a person buys milk and bread, then they are likely to also get some eggs. These insights are built upon previous purchases from various clients. Association rules are then formed according to a specific threshold for confidence set by the algorithm based on how frequently these items are brought together.
Ensembling algorithms are supervised algorithms made of combining the prediction of two or more other machine learning algorithms to produce more accurate results. Combining the results can either be done by voting or averaging the results. Voting is often used during classification and averaging during regression.
- The only reason to ever need to use such a weapon would be in the face of overwhelming ground forces that were threatening to overrun important NATO positions in Europe. The idea was to launch Davy Cr