Create Readme.md

Author: tirthajyoti

Complexity_Learning_curves/Readme.md

@@ -0,0 +1,21 @@
+## Complexity and Learning curves
+Complexity and learning curve analyses are some of the most important tasks in a Machine Learning project.
+
+**Learning curve**: Graphs that compares the performance of a model on training and testing data over a varying number of training instances. 
+
+We should generally see performance improve as the number of training points increases. 
+
+**Complexity curve**: Graphs that show the model performance over training and validation set for varying degree of model complexity 
+(e.g. degree of polynomial for linear regression, number of layers or neurons for neural networks, 
+number of estimator trees for a Boosting algorithm or Random Forest)
+
+Complexity curve allows us to verify when a model has learned as much as it can about the data without fitting to the noise. 
+The optimum learning (given the fixed data) occurs when,
+
+* The performances on the training and testing sets reach a plateau
+* There is a consistent gap between the two error rates
+
+The key is to find the sweet spot that minimizes bias and variance by finding the right level of model complexity.
+Of course with more data any model can improve, and different models may be optimal.
+
+### Following is the intuitive illustration of model complexity curve from Andrew Ng's Machine Learning course