Recap from Day 007
In day 007, we continued with the common classification algorithms, by looking at Support Vector Machine and Naive Bayes. Today, we’ll continue from where we stopped.
Common Classification Algorithms continued
A computer system modelled on the human brain and nervous system. Inspired by the human brain, a neural network consists of highly connected networks of neurons that relate the inputs to the desired outputs. The network is trained by iteratively modifying the strengths of the connections so that given inputs map to the correct response.
For modeling highly nonlinear systems
When data is available incrementally and you wish to constantly update the model
When there could be unexpected changes in your input data
When model interpretability is not a key concern
As the name goes, it uses a tree-like model of decisions. A decision tree lets you predict responses to data by following the decisions in the tree from the root (beginning) down to a leaf node. A tree consists of branching conditions where the value of a predictor is compared to a trained weight. The number of branches and the values of weights are determined in the training process. Additional modification, or pruning, may be used to simplify the model.
When you need an algorithm that is easy to interpret and fast to fit
To minimize memory usage
When high predictive accuracy is not a requirement
You made it to the end of day 008. Thank you for taking time out of your schedule and allowing me to be your guide on this journey.
Refrence: Mathworks 90221_80827v00_machine_learning_section4_ebook_v03.pdf