Best and Worst Cases of Machine Learning Algorithms ?

KNN Algorithms :

Best Cases :

• If dimensionality (no of features) is low then this
works best.

• If you know the right distance measure then
KNN is a good option.

Worst Cases :

• If dimensionality (no of features) is low then this
works best.

• If you know the right distance measure then
KNN is a good option.

Naive Bayes Algorithm :

*Best Cases :*

• If the Conditional independence assumption of
naive Bayes is true then it performs very well.

• Naive Bayes is the default algorithm when solving
text classification problems.

• Naive Bayes is often used when you have
categorical features (binary)

• Great interpretability, feature importance, low run
time.

Worst Cases :

• If the Conditional independence assumption of
naive Bayes is false then its performance
deteriorates.

• Naive Bayes is not often used when you have real
value features.

• Easily get's overfitted when Laplace smoothing is
not done correctly.

Logistic Regression Algorithm :

Best Cases :

• It works best when data is almost linearly
separable and it is good if you have a low latency
requirement.

• It is good for interpretability and feature
importance using weights (coefficients).

• Less impact of outliers because of the sigmoid.

• If dimensionality is large it works well.

Worst Cases :

• It works badly when data is not linearly separable.

• When data is imbalanced.

• Missing values.

• No multi-class classification for the base model.

• When multicollinearity exists these models will
not work properly.

Linear Regression Algorithm :

Best Cases :

• It is good for interpretability and feature
importance using weights (coefficients).

• If Feature engineering is done then this model
can work better.

Worst Cases :

• Outliers impact a lot.

• When multicollinearity exists these models will
not work properly.

SVM Algorithm :

Best Cases :

• If you can find the right kernel then thins works at its
best.

• This can be applied to non-linear problems.

• Interpretability and feature importance is easy for
linear SVM's.

• The impact of outliers is less.

• If dimensionality is large then SVM works like a charm.

Worst Cases :

•Interpretability and feature importance is hard for
kernel SVM's.

• If training data is large, training time is high.

Decision Tree Algorithm :

Best Cases :

• Multi-class classification is possible.

• Interpretability and feature importance.

Worst Cases :

• Imbalanced data impacts a lot.

• If dimensionality is large then training time is high.

• If you use one-hot encoding then training time will
be high.

• Outliers will impact the model.