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Issues in Decision Tree Learning
Overfitting Overfitting happens when the learning algorithm continues to develop hypothesis that reduce training set error at the cost of an increased test set error. Approach to avoid Overfitting Pre-pruning that stops growing the tree earlier, before it perfectly classifies the training set. Post-pruning allows the tree to perfectly classify the training set, and then…
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What Are The Steps In Developing A Machine Learning Applications ?
Collect Data This include collecting the samples by scraping a website and extracting data, from an RSS feed or an API. Use necessary sensors or make use of publicly available data. Prepare Input Data Once data is collected make sure its in a useable format. The benefit of having this standard format is that you…
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Types of Machine Learning
Supervised Learning Learning process with the help of a teacher is called supervised learning. In supervised learning the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn a function which maps a vector into one of…
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Questions That Machine Learning Still Need To Answer
What algorithms exist for learning general target from specific training example ? 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 ? How much training data is sufficient ? What general bounds can be found to relate…
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What is Machine Learning ?
Machine Learning can be defined as a branch of Artificial Intelligence that has the ability to learn on its own without having all information with respect to a domain in the program itself. Machine Learning is a branch or say sub branch of Artificial Intelligence (AI) that provides computers with the ability to learn without…