Advantages and disadvantages of machine learning algorithms and summary of applicable scenarios

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1.k-Nearest Neighbor algorithm

Advantages: High precision, insensitive to outliers, no data input settings

Cons: High computational complexity, high spatial complexity

Applicable data range: Numerical and nominal type

Applicable scenarios:

2.ID3 Decision Tree Algorithm

Advantages: The computational complexity is not high, the output is easy to understand, the missing middle value is not sensitive, can process the irrelevant characteristic data

Disadvantage: May cause over-matching problems

Applicable data type: nominal type

Applicable scenarios:

3. Naive Bayes

Advantage: Still effective in the case of less data, can handle multi-category problems

Disadvantage: Sensitive to the way the input data is prepared

Applicable data type: Nominal type data

Application Scenario: Document classification

Advantages and disadvantages of machine learning algorithms and summary of applicable scenarios