profit, reward

Zero-sum is a situation in game theory in which one person’s gain is equivalent to another’s loss, so the net change in wealth or benefit is zero.

Expected value is a measure of what you should expect to get per game in the long run. The payoff of a game is the expected value of the game minus the cost. If you expect to win about $2.20 on average if you play a game repeatedly and it costs only $2 to play, then the expected payoff is $0.20 per game.

The basic expected value formula is the probability of an event multiplied by the amount of times the event happens: (P(x) * n). The formula changes slightly according to what kinds of events are happening.

In statistics: Decision analysis. …more possible future events, called states of nature, that might occur. The list of possible states of nature includes everything that can happen, and the states of nature are defined so that only one of the states will occur.

In the decision making process, “states of nature” are the non-business factors in the environment in which the decision must be made. the factors that are discovered after a decision is made that could not have been anticipated. the non-human factors in the decision situation that a decision maker must consider.

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

Apart from overfitting, Decision Trees also suffer from following disadvantages: 1. Tree structure prone to sampling – While Decision Trees are generally robust to outliers, due to their tendency to overfit, they are prone to sampling errors.

Why are decision tree classifiers so popular ? Decision tree construction does not involve any domain knowledge or parameter setting, and therefore is appropriate for exploratory knowledge discovery. Decision trees can handle multidimensional data.

increased test set error. There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.

How to Prevent Overfitting or Underfitting

1. Cross-validation:
2. Train with more data.
3. Data augmentation.
4. Reduce Complexity or Data Simplification.
5. Ensembling.
6. Early Stopping.
7. You need to add regularization in case of Linear and SVM models.
8. In decision tree models you can reduce the maximum depth.

Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.