Supervised Learning Concepts & its algorithm
Major Issues of Supervised Learning & Learn features of Naive Bayes-Supervised Classification Learning Approach
Major Issues of Supervised Learning
- Bias-variance tradeoff
- Function complexity and amount of training data
- Dimensionality of the input space
- Noise in the output values
- Computational learning theory
- Inductive bias
- Overfitting (machine learning)
- Class membership probabilities
- Version spaces
Naive Bayes (Supervised classification learning)
It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Naive Bayesian model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
Naive Bayes classifier algorithm assumes that two events are independent of each other and thus, this simplifies the calculations to a large extent.
Naive Bayes algorithm can be used to find simple relationships between different parameters without having complete data.
For example, to check the probability that you will be late to the office, one would like to know if you face any traffic on the way.
Click on the below YouTube video to understand the concept better.
If you are running short of time and want a compiled QUICK REVISION NOTES , then click HERE to download.
