Principal Component Analysis Solved Example Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. In this article, I will discuss how to find the principal components with a simple …
Introduction to Principal component analysis in Machine Learning Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the …
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The complexity of any classification or regression algorithm depends on the number of inputs to the model. This determines the time and space complexity and the necessary number of training examples to train such a classification or regression algorithm. In this article, we discuss what is dimensionality reduction, how dimensionality reduction is implemented, and the …
Approaches to Artificial Intelligence Turing Test and Rational Agent Approaches According to latest research, definitions of artificial intelligence vary along main dimensions thought processes and reasoning, and behavior. Thus views of Al fall into four categories Thinking Humanly (The Cognitive approach) Acting Humanly (The Turing Test approach) Thinking Rationally (The Laws of Thought approach) Acting …
Introduction to Artificial Intelligence What is Artificial Intelligence (Al)? Artificial Intelligence (Al) is a branch of Science which deals in development of modern machines, which can find solutions to complex problems in human like fashion Artificial intelligence basically works on insulting human intelligence characteristics, and algorithms m a computer so that machine can behaves like …
Explain the concepts of Entropy and Information Gain in Decision Tree Learning. While constructing a decision tree, the very first question to be answered is, Which Attribute Is the Best Classifier? The central choice in the ID3 algorithm is selecting which attribute to test at each node in the tree. We would like to select …
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What are appropriate problems for Decision tree learning? Although a variety of decision tree learning methods have been developed with somewhat differing capabilities and requirements, decision tree learning is generally best suited to problems with the following characteristics: 1. Instances are represented by attribute-value pairs. “Instances are described by a fixed set of attributes (e.g., …
What are decision tree and decision tree learning? Explain the representation of the decision tree with an example. Decision Trees is one of the most widely used Classification Algorithm Features of Decision Tree Learning Method for approximating discrete-valued functions (including boolean) Learned functions are represented as decision trees (or if-then-else rules) Expressive hypotheses space, including …
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Perspectives and Issues in Machine Learning Following are the list of issues in machine learning: 1. What algorithms exist for learning general target functions from specific training examples? In what settings will particular algorithms converge to the desired function, given sufficient training data? Which algorithms perform best for which types of problems and representations? 2. …
Consistent Hypothesis, Version Space and List then Eliminate Algorithm Consistent Hypothesis The idea: output a description of the set of all hypotheses consistent with the training examples (correctly classify training examples). Version Space: a representation of the set of hypotheses that are consistent with D an explicit list of hypotheses (List-Then-Eliminate) a compact representation of …
