Bias and Variance trade off:

Bias-Variance trade off is better understood with darts. I guess everybody is familiar with darts where you throw a small arrow to the circular board and try to stick it in the center of the circular board or also called the bull’s eye. Now imagine you threw 10 such arrows.
There are 4 possibilities:
1.   all 10 arrows are scattered but in the center, which is called low bias high variance
2.   all 10 arrows are together near to each other but away from the center also called high bias low variance.
3.   all 10 arrows are away from the center and scattered well also called high variance high bias.
4.   all 10 arrows are near the center and not scattered more also called low variance and low bias which is what we need.
Generally, parametric algorithms have a high bias making them fast to learn and easier to understand but generally less flexible. In turn, they have lower predictive performance on complex problems that fail to meet the simplifying assumptions of the algorithms bias.

Decision trees are an example of a low bias algorithm, whereas linear regression is an example of a high-bias algorithm.

Comments

Post a Comment

Popular posts from this blog

Boosting and AdaBoost algorithm.

Image
 Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. This is done by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. Models are added until the training set is predicted perfectly or a maximum number of models are added. AdaBoost was the first really successful boosting algorithm developed for binary classification. It is the best starting point for understanding boosting. Modern boosting methods build on AdaBoost, most notably stochastic gradient boosting machines. AdaBoost is used with short decision trees. After the first tree is created, the performance of the tree on each training instance is used to weight how much attention the next tree that is created should pay attention to each training instance. Training data that is hard to predict is given more weight, whereas easy to predict instances are given less weight. Mode...
Read more

Decision tree

Image
A decision tree is a schematic, tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision, occurrence or reaction. The tree is structured to show how and why one choice may lead to the next, with the use of the branches indicating each option is  mutually exclusive . x Decision Trees are an important type of algorithm for predictive modeling machine learning. The representation of the decision tree model is a binary tree. This is your binary tree from algorithms and data structures, nothing too fancy. Each node represents a single input variable (x) and a split point on that variable (assuming the variable is numeric). A decision tree is faster as compared to linear algorithms because it is generally faster. Trees work with nodes and can be fast as compared to other linear algorithms. The leaf nodes of the tree contain an output variable (y) which is used to make a...
Read more

Random forest algorithm

Image
Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. The bootstrap is a powerful statistical method for estimating a quantity from a data sample. Such as a mean. You take lots of samples of your data, calculate the mean, then average all of your mean values to give you a better estimation of the true mean value. In bagging, the same approach is used, but instead for estimating entire statistical models, most commonly decision trees. Multiple samples of your training data are taken then models are constructed for each data sample. When you need to make a prediction for new data, each model makes a prediction and the predictions are averaged to give a better estimate of the true output value. Random forest is a tweak on this approach where decision trees are created so that rather than selecting optimal split points, suboptimal splits are made by intro...
Read more