## Evaluating a Learning Algorithm

### Deciding what to try next

- We know many learning algorithm
- But,how to choose the best algorithm to explore the various techniques
- Here we focus deciding what avenues to try

- We know many learning algorithm
- But,how to choose the best algorithm to explore the various techniques
- Here we focus deciding what avenues to try

- L = total number of layers in the network
- s
_{ls}= number of units (not counting bias unit) in layer l - K = number of output units/classes

- Consider a supervised learning classification problem
- logistic regression
- g as usual is sigmoid function
- And, if you include enough polynomial terms then, you know, maybe you can get a hypotheses.
- However, this problem is just about two features x
_{1}and x_{2}, many machine learning problems would have a lot more features.

- e.g. our housing example
- 100 house features, predict odds of a house being sold in the next 6 months
- Here, if you included all the quadratic terms (second order)
- There are lots of them (x
_{1}^{2},x_{1}x_{2}, x_{1}x_{4}..., x_{1}x_{100}) - For the case of n = 100, you have about 5000 features
- Number of features grows O(n
^{2})

- There are lots of them (x

- Not a good way to build classifiers when n is large

y is a discrete value

Variable in these problems is Y

- Y is either 0 or 1
- 0 = negative class (absence of something)
- 1 = positive class (presence of something)

- Start with binary class problems
- Later look at multiclass classification problem

- Y is either 0 or 1

- Multiple variables = multiple features
- If in a new scheme we have more features to predict the price of the house
- x
_{1}, x_{2}, x_{3}, x_{4}are the four features- x1 - size (feet squared)
- x2 - Number of bedrooms
- x3 - Number of floors
- x4 - Age of home (years)

- y is the output variable (price)

- x

- Two definitions:
- Arthur Samuel(1959)
- "The field of study that gives computers the ability to learn without being explicitly programmed."
- This is an older, informal definition.

- Tom Mitchell(1999)
"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

The checkers example.- E = the experience of playing many games of checkers
- T = the task of playing checkers.
- P = the probability that the program will win the next game.

- Arthur Samuel(1959)
- Several types of learning algorithms
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Recommender systems

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