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\nEvaluate the accuracy of an artificial intelligence system : Pointers on Evaluating the Accuracy of Classification Modelling\n

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\nFrom the section: Supervised Learning: Classification. In this section, the author talks about kNN- Classification, Naive Bayes Classification, SVM- Linear and Non-Linear Classification and also Gradient Boosting Machine...\nFrom the section: Supervised Learning: Classification. In this section, the author talks about kNN- Classification, Naive Bayes Classification, SVM- Linear and Non-Linear Classification and also Gradient Boosting Machine (GBM).

Supervised Learning: Classification: Pointers on Evaluating the Accuracy of Classification Modelling\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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9:42
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Grade
Higher Ed
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Subjects
Math
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Media Type
Instructional Videos
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Source:
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\nPackt - Data\n\n
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Date
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2021
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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computer science, data, accuracy, classification, values, work, coding
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Additional Tags

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computer engineering, compsci, programming, development, response variable, make sense, confusion matrix, true positives, false positives, true negatives, false negatives, data set, training data, accuracy score, binary classifications, multiclass classification, test data, training model, models, overfitting, regression, evaluating, ability, predicted, model, build, talk, lectures
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Best For: Explaining a topic
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