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.