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What is machine learning model?
What is machine learning model?
A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
There are many models nowadays such as linear regression, decision trees, random forest, Naive Bayes, kNN. However, in this article, will only focus on Naive Bayes, KNN, SVM, and Decision Tree
Train the model based on the collected data ->
-> Evaluate it based on their predictions results
-> if it is good, proceed to this model and apply it to more new data
-> Success!!!
Naive Bayes
Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks
KNN
Uses proximity (nearby) to classify or predict the grouping of the data point. Used for classification or regression problem.
SVM
Used for classification, regression and outliers' detection
Decision Tree
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).
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