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Machine Learning Algorithms, As we all know, machine learning is rapidly evolving, and many individuals are pursuing professions in the field.
Furthermore, a Harvard Business Review article dubbed “Data Scientist” the “Sexiest Job of the Twenty-First Century.” Because understanding algorithms can make our life easier.
As a newbie, you might be wondering what the Top 5 Machine Learning Algorithms are.
Before going into the actual algorithms, it’s important to first grasp the many types of machine learning algorithms.
Supervised Machine Learning Algorithm- We have training data in a supervised machine learning algorithm, which means we train our model using a labeled dataset.
We used labeled data to train our model, and now we’re going to use some additional data to predict the outcome. The Classification algorithm is another name for it.
Unsupervised Machine Learning Algorithm- We don’t have any training data in an unsupervised machine learning algorithm, thus the model has to learn on its own without any prior information. The Clustering algorithm is another name for it.
Reinforcement Machine Learning Algorithms– Reinforcement learning is based on hit and trial, which means that the model learns from its own failures and improves its accuracy, similar to how video games work.
Machine Learning Algorithms Top 5
Now that we’ve covered the many types of machine learning, let’s look at the top 5 easy and reliable machine learning algorithms.
Manually classifying data, such as papers, emails, or web pages, is difficult, but the Nave Bayes Classifier makes it simple.
The Bayes Theorem of Probability is used in the Naive Bayes Classifier Algorithm, which assigns the population’s element value to one of the possible categories.
Spam filtering and sentiment analysis are two applications of the Naive Bayes Classifier.
If you have a huge dataset, the Naive Bayes Classifier is the way to go.
2. Support Vector Machine Learning Algorithm–
The Support Vector Machine (SVM) is a machine learning algorithm that is supervised. It solves problems involving classification and regression.
In SVM, the data is divided into separate classes by a hyperplane (a line).
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SVM aims to locate the hyperplane that maximizes the distance between distinct classes, a process known as margin maximization, which increases the likelihood of correctly classifying data.
Stock market prediction is one of the applications of SVM.
Clustering methods with the letter K divide each observation into a K cluster.
Unsupervised learning is what it is. Clustering is done by it. It starts with randomly selected centroids, as it is a type of unsupervised learning, and selects random K points that are centroids.
The centroid is a virtual or exact point that represents the cluster’s center.
The data is then assigned to the closest centroid. Depending on the distance type, you can use the Euclidean distance formula or any other distance formula.
For each cluster, the K means clustering method calculates and locates a new centroid.
After that, they reassign data points based on the new closet centroid, and if there is any reassignment, they repeat the previous procedure until the model is complete.
Clustering is an iterative process, as indicated by the letter K.
4. Apriori Algorithm
The association rules are the foundation of the Apriori algorithm. For instance, suppose someone who purchased milk also purchased bread.
The Apriori algorithm is also known as association rule learning.
The Apriori algorithm is most commonly used in marketing since it allows for the analysis of purchase patterns.
If someone buys milk and bread together, for example, but the two items together in a supermarket to promote sales of both. A person who comes to buy milk may also purchase bread if both goods are together.
Support, Confidence, and Lift are the three terms used by the Apriori algorithm.
Support (in terms of milk and bread Example) =Customer who purchased milk/total number of customers
Customer confidence = total customers who bought milk and bread.
Confidence/Support = Lift
In both classification and regression, a decision tree is used.
A decision Tree is a tree-like structure that starts at the root node and ends at the leaf node, as the name suggests.
The decision tree’s internal or non-leaf node represents a feature test, while the leaf node acts as a class label.
The decision tree evaluates each node’s condition before moving on.
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