# 'Decision tree' diaporamas de présentation

## An Introduction to Text Mining

Ravindra Jaju An Introduction to Text Mining Outline of the presentation Initiation/Introduction ... What makes text stand apart from other kinds of data? Classification Clustering Mining on “The Web” Data Mining What: Looking for information from usually large amounts of data

By oshin
(591 views)

## Binary Decision Tree

Binary Decision Tree. Computer Science and Engineering. Introduction. Binary Search Tree: Key value at the root partitions the set of data represented in the tree into three disjoint sets: A set with key value at the root A set of keys in the left subtree < key value of the root

By Roberta
(356 views)

## Joya’s Birthday Problem…

Joya’s Birthday Problem…. Joya's tenth birthday is in just a few days… She has narrowed her birthday party ideas to: Pizza party at Palermo’s Palace, or Ice cream, cake and presents at home…. Joya’s Birthday Problem…. Joya's mother says:

By Michelle
(294 views)

## Recall that we have seen an example of feature generation .

Recall that we have seen an example of feature generation . In my experience, doing feature generation can often be the key to solving problems, so lets do a worked example…. What features can we use classify Japanese names vs Irish names?.

By Lucy
(276 views)

## Inductive Learning from Imbalanced Data Sets

Inductive Learning from Imbalanced Data Sets. Nathalie Japkowicz, Ph.D. School of Information Technology and Engineering University of Ottawa . Inductive Learning: Definition.

By Lucy
(344 views)

## Introduction to Machine Learning

Introduction to Machine Learning. Reading for today: R&N 18.1-18.4 Next lecture: R&N 18.6-18.12, 20.1-20.3.2. Outline. The importance of a good representation Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve Bayes

By ostinmannual
(320 views)

## CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Bart Selman selman@cs.cornell.edu Machine Learning: Decision Trees R&N 18.3. Big Picture of Learning. Learning can be seen as fitting a function to the data. We can consider

By Mia_John
(200 views)

## Machine Learning Chapter 3. Decision Tree Learning

Machine Learning Chapter 3. Decision Tree Learning. Tom M. Mitchell. Abstract. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Decision Tree for PlayTennis. A Tree to Predict C-Section Risk. Learned from medical records of 1000 women

By Rita
(368 views)

## Decision Trees and Information: A Question of Bits

Decision Trees and Information: A Question of Bits. Choice Tree. A choice tree is a rooted, directed tree with an object called a “choice” associated with each edge and a label on each leaf. Choice Tree Representation of S. We satisfy these two conditions: Each leaf label is in S

(171 views)

## Random Variables & Entropy: Extension and Examples

Random Variables & Entropy: Extension and Examples. Brooks Zurn EE 270 / STAT 270 FALL 2007. Overview. Density Functions and Random Variables Distribution Types Entropy. Density Functions. PDF vs. CDF PDF shows probability of each size bin

By betty_james
(415 views)

## Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement

Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement. Aswath Damodaran Updated: January 2013. Real Options: Fact and Fantasy. Aswath Damodaran. Underlying Theme: Searching for an Elusive Premium.

By una
(190 views)

## Java Buzzwords

Java Buzzwords. Java!. Java is a language characterized by buzzwords buzzword: A trendy word or phrase that is used more to impress than explain From Sun Microsystems , the developers of Java: What do all of those terms mean?. “Java is a simple, object-oriented, distributed,

By verdi
(225 views)

## Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement

Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement. Aswath Damodaran Updated: September 2014. Real Options: Fact and Fantasy. Aswath Damodaran. Underlying Theme: Searching for an Elusive Premium.

By melvina
(118 views)

## Decision Trees

Decision Trees. What is a decision tree?. Input = assignment of values for given attributes Discrete (often Boolean) or continuous Output = predicated value Discrete - classification Continuous - regression Structure: Internal node - tests one attribute

By owena
(594 views)

## Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement

Valuation: Packet 3 Real Options, Acquisition Valuation and Value Enhancement. Aswath Damodaran Updated: January 2019. Real Options: Fact and Fantasy. Aswath Damodaran. Underlying Theme: Searching for an Elusive Premium.

By royal
(373 views)

## Machine Learning” Notes 2

Machine Learning” Notes 2. Dr. Alper Özpınar. Training , Validating and Testing  Data.

By vilmaris
(297 views)

## QS 726 – Quality Systems

QS 726 – Quality Systems. Organization and Implementation of a New Quality System using LSSQTT. Phase II. Fall 2006. Overview. Participants Course Outcomes Course Focus LSSQTT Tools Reviewed LSSQTT Tool Outcomes Project Outcomes Project Description & Statement Project Approach

By percy
(162 views)

## Data Mining CSCI 307 Spring, 2019

Data Mining CSCI 307 Spring, 2019. Lecture 2 Describing Patterns Simple Examples. Data versus Information. Society produces huge amounts of data Sources : business, science, medicine, economics, geography, environment, sports, … Potentially valuable resource

By alissa
(188 views)

## PWG Update Report By Brad Boles of Cirro Energy ERCOT PWG Vice-Chair for COPS Meeting May 13, 2008

PWG Update Report By Brad Boles of Cirro Energy ERCOT PWG Vice-Chair for COPS Meeting May 13, 2008. Updated PWG 2008 Goals (ranked by Target Date). Round 2 Sample Point Installation Tracking Percent New Installations as of May 5, 2008.

By landry
(101 views)

## Identifying glass

Identifying glass. Using the top-notch data-mining algorithms from the Leiden Institute of Advanced Computer Science (LIACS) Presented by Jan-Willem and Frans-Willem. Why identifying glass?. Crime scene: glass found in leg of murdered person. Where did the glass come from?

By carver
(145 views)

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