Machine Learning: The Art and Science of Algorithms that Make Sense of Data

As probably the most accomplished laptop studying texts round, this publication does justice to the field's fantastic richness, yet with out wasting sight of the unifying rules. Peter Flach's transparent, example-based strategy starts off by way of discussing how a unsolicited mail filter out works, which supplies a right away creation to computer studying in motion, with at the least technical fuss. Flach offers case experiences of accelerating complexity and diversity with well-chosen examples and illustrations all through. He covers a variety of logical, geometric and statistical types and state of the art subject matters akin to matrix factorisation and ROC research. specific awareness is paid to the vital function performed through positive aspects. using confirmed terminology is balanced with the creation of recent and precious ideas, and summaries of suitable heritage fabric are supplied with tips for revision if helpful. those gains be sure computing device studying will set a brand new typical as an introductory textbook.

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Forty eight 2 Binary classification and similar initiatives 2. 1 forty nine Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fifty two ix Contents x Assessing classification functionality . . . . . . . . . . . . . . . . . . . . . . fifty three Visualising classification functionality . . . . . . . . . . . . . . . . . . . . . fifty eight 2. 2 Scoring and rating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . sixty one Assessing and visualising score functionality . . . . . . . . . . . . . . . . sixty three Turning rankers into classifiers . . . . . . . . . . . . . . . . . . . . . . . . . sixty nine 2. three category chance estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . seventy two Assessing category chance estimates . . . . . . . . . . . . . . . . . . . . . . seventy three Turning rankers into type likelihood estimators . . . . . . . . . . . . . . . seventy six 2. four three Binary classification and comparable initiatives: precis and additional studying . . seventy nine past binary classification three. 1 eighty one dealing with greater than sessions . . . . . . . . . . . . . . . . . . . . . . . . . eighty one Multi-class classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . eighty two Multi-class ratings and chances . . . . . . . . . . . . . . . . . . . . . . 86 three. 2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ninety one three. three Unsupervised and descriptive studying . . . . . . . . . . . . . . . . . . . . ninety five Predictive and descriptive clustering . . . . . . . . . . . . . . . . . . . . . . ninety six different descriptive versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a hundred three. four four past binary classification: precis and additional studying . . . . . . . . 102 idea studying four. 1 104 The speculation house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Least common generalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 inner disjunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a hundred and ten four. 2 Paths throughout the speculation area . . . . . . . . . . . . . . . . . . . . . . 112 such a lot normal constant hypotheses . . . . . . . . . . . . . . . . . . . . . . 116 Closed thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 four. three past conjunctive strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 119 utilizing first-order common sense . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 five four. four Learnability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 four. five idea studying: precis and additional examining . . . . . . . . . . . . . . . 127 Tree versions 129 five. 1 determination timber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 five. 2 score and likelihood estimation timber . . . . . . . . . . . . . . . . . . . 138 Sensitivity to skewed classification distributions . . . . . . . . . . . . . . . . . . . . 143 five. three Tree studying as variance relief . . . . . . . . . . . . . . . . . . . . . . . 148 Regression timber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Contents xi Clustering timber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 five. four 6 Tree types: precis and additional analyzing . . . . . . . . . . . . . . . . . . a hundred and fifty five Rule types 6. 1 157 studying ordered rule lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Rule lists for score and chance estimation . . . . . . . . . . . . . . . 164 6. 2 studying unordered rule units . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Rule units for score and chance estimation . . . . . . . . . . . . . . . 173 a more in-depth examine rule overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6. three Descriptive rule studying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Rule studying for subgroup discovery . . . . . . . . . . . . . . . . . . . . . . 178 organization rule mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 7 6. four First-order rule studying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 6. five Rule types: precis and additional analyzing .

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