Classification algorithm in machine learning

Example: Classification of types of crops, Classification of types of music.

Types of classification algorithms in machine learning:
Classification algorithms can be mainly divided into two categories:

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  Linear models
  

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  Logistic regression
  
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  Support vector machines
  

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  Non-linear models
  

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  K-Nearest Neighbors
  
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  Kernel SVM
  
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  Naive Bayes
  
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  Decision tree classification
  
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  Random forest classification
  

Evaluating a classification model in machine learning:
Once our model is complete, we need to evaluate its performance.
The most important task in building any machine learning model is to evaluate its performance. So the question is: How do you measure the success of a machine learning model? How do we know when to stop training and assessment and when to call it good?

For a given sample observation, the true class is either positive or negative. Similarly, the predicted class is either positive or negative.
There are four categories related to the actual and predicted class of observations:
True Positive (TP): Both the actual and predicted values of a given observation are positive.
False positive (FP): A given observation is negative, but the predicted value is positive.
True Negative (TN): Both the actual and predicted values of a given observation are negative.
False negative (FN): A given observation is predicted to be negative even though it is actually positive.
FP and FN are classification errors . In statistics, FPs are called Type I errors and FNs are called Type II errors. In some cases, type II errors are dangerous.
For example, if our classifier predicts a house fire, a Type I error is a false alarm.
On the other hand, a Type II error means that the house is on fire and the fire department is not aware.