#!/usr/bin/env python
# coding: utf-8

# In[1]:


### METHOD - 1
##### Train/Test Split

# We train our model on the Train set and Evaluate on the Test set. 
# Typical fractions are 7:3 for train and test respectively.
# Sklearn provides a method: train_test_split(X, Y, test_size=0.3, random_state=10) for this purpose
# random_state is required to reproducing the results.
# Very Fast


# In[2]:


### METHOD - 2
##### K-Fold Cross Validation

# We split our dataset into K folds (typical values are 3, 5, 10)
# Algorithm is trained on K-1 folds, where 1 fold is held back and testing happens on that held back fold.
# After running cross-validation you end up with k different performance scores that you can summarize
# using a mean and a standard deviation.
# Sklean provides KFold(n_splits=5, random_state=10) method for this purpose


# In[3]:


### METHOD - 3
##### Leave One Out Cross Validation

# We make 1 Fold of our dataset containing all the N datapoints.
# We train our algorithm on N-1 points and predicts tha left out point.
# N different  performance scores that you can summarize
# Sklearn provides LeaveOneOut() method for this purpose
# Computationaly intensive


# In[4]:


### METHOD - 4
##### Repeated Random Train-Test Splits

# Inspired by K-fold.
# It's simply METHOD - 1 that is run N number of times with different random seed split of data.
# Sklearn provides ShuffleSplit(n_splits=5, test_size=0.3, random_state=10) for this purpose