Decision Tree Learning

Decision Tree Learning is one of the most widely used and practical methods for inductive inference. Decision tree learning is one of the most successful techniques for supervised classification learning.

Decision Tree Learning Algorithms:

  1. ID3
  2. ASSISTANT
  3. C4.5

When to use Decision Tree Learning:

  • Instances are represented by attribute-value pairs
  • The Target function has discrete output values
  • Disjunctive descriptions may be required
  • The training data may contain errors
  • The training data may contain missing attribute values

Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” — Tom Mitchell, Carnegie Mellon University

So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P).

Types of Machine Learning:

  • Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
    • ClassificationClassification is supervised learning technique used to assign per-defined tag to instance on the basis of features. So classification algorithm requires training data. Classification model is created from training data, then classification  model is used to classify new instances.The task of assigning instances to pre-defined classes.
      • suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place.
      • suppose the fruits are apple,banana,cherry,grape.
      • so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place.
      • here your previous work is called as train data in data mining.
      • so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit.
  • Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
    • ClusteringClustering is unsupervised technique used to group similar instances on the basis of features. Clustering does not require training data. Clustering does not assign per-defined label to each and every group. The task of grouping related data points together without labeling them.
      • suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place.
      • This time you don’t  know any thing about that fruits, you are first time seeing these fruits so how  will you arrange the same type of fruits.
      • What you will do first you take on fruit and you will select any physical character of that particular fruit. suppose you taken color.
      • Then you will arrange them base on the color, then the  groups will be some thing like this.
      • RED COLOR GROUP: apples & cherry fruits.
      • GREEN COLOR GROUP: bananas & grapes.
      • so now you will take another physical character as size, so now the groups will be some thing like this.
      • RED COLOR AND BIG SIZE: apple.
      • RED COLOR AND SMALL SIZE: cherry fruits.
      • GREEN COLOR AND BIG SIZE: bananas.
      • GREEN COLOR AND SMALL SIZE: grapes.
      • job done happy ending.
      • here you didn’t know learn any thing before means no train data and noresponse variable.

Courtesy:

http://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

http://www.quora.com/What-is-the-difference-between-Clustering-and-Classification-in-Machine-Learning

Deductive Learning vs Inductive Learning

Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a “top-down” approach. We might begin with thinking up a theoryabout our topic of interest. We then narrow that down into more specifichypotheses that we can test. We narrow down even further when we collect observations to address the hypotheses. This ultimately leads us to be able to test the hypotheses with specific data — a confirmation (or not) of our original theories.

deduct

Deductive reasoning is a basic form of valid reasoning. Deductive reasoning, or deduction, starts out with a general statement, or hypothesis, and examines the possibilities to reach a specific, logical conclusion.

Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. Informally, we sometimes call this a “bottom up” approach (please note that it’s “bottom up” and not “bottomsup” which is the kind of thing the bartender says to customers when he’s trying to close for the night!). In inductive reasoning, we begin with specific observations and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up developing some general conclusions or theories.

induct

Inductive reasoning is the opposite of deductive reasoning. Inductive reasoning makes broad generalizations from specific observations.

Inductive inference is the process of reaching a general conclusion from specific examples.

Inductive Learning Hypothesis: any hypothesis found to approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples.

The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered (Mitchell, 1980).

Courtesy:

http://www.socialresearchmethods.net/kb/dedind.php

http://www.livescience.com/21569-deduction-vs-induction.html

http://www2.cs.uregina.ca/~dbd/cs831/notes/ml/2_inference.html