Search for tag: "classifier"

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16:23 Flood Risk: Juliette Legrand Flood Risk: Juliette Legrand	Flood Risk: Juliette Legrand This talk has been automatically captioned. You can remove these by pressing CC on the video toolbar. Name: Juliette Legrand Talk Title: Evaluation of binary classifiers for extremes Abstract:… eva classifier function risk classifiers epsilon dependence mass axis event station formula extremes loss structure bar observation river linear dots h. From Belle Taylor on July 1st, 2021 0 likes 23 plays 0 Publisher Centre for Statistics Licence Type All rights reserved Language English Date Created July 1st, 2021
03:28 Juliette Legrand EVA Talk Preview Juliette Legrand EVA Talk Preview	Juliette Legrand EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Juliette Legrand Talk Title: Evaluation of binary classifiers for extremes Abstract: Machine learning… solution theory icms eva legrand evaluation binary classifiers extremes classifier paper function risk walk term idea linear observation x. discharges river prediction power byproduct methods machine appetiser From Anna Munro on May 13th, 2021 0 likes 11 plays 0 Licence Type All rights reserved Language English
11:17 FDS-S2-02-2-4 The logistic regression classifier FDS-S2-02-2-4 The logistic regression classifier	FDS-S2-02-2-4 The logistic regression classifier We show how to make a logistic regression classifier, and, in the context of ethics, consider how transparent logistic regression can be made to people whose lives it affects. We also compare… inf2-fds regression log boundary probability credit decision classifier neighbours odds neighbour income ratio variables algorithm score customers approval beta linear age From David Sterratt on January 20th, 2021 0 likes 163 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial No Derivatives Language English Date Created January 20th, 2021
09:48 ML5: K-Nearest Neighbours Classification ML5: K-Nearest Neighbours Classification	ML5: K-Nearest Neighbours Classification In this video, we introduce the simplest classifier: the K-Nearest Neighbours classifier. This classifier has no trainable parameters, and classifies test points by finding its closest training… test point training neighbors class points classifier validation classification parameters hyperparameters vector dog george distance spam cat jackson vote step From Elliot Crowley on November 9th, 2020 0 likes 11 plays 0
11:13 ML7: Logistic Classification ML7: Logistic Classification	ML7: Logistic Classification Here, we introduce a logistic classifier and cover how it differs from a linear classifier. Then, we show how its parameters can be learnt by minimising the cross-entropy loss. Finally, we visualise… classifier loss class function probability points point parameters label outputs i. training linear boundary decision side contribution times minus thing From Elliot Crowley on November 6th, 2020 0 likes 10 plays 0
10:51 ML6: Linear Classification ML6: Linear Classification	ML6: Linear Classification In this video, we are introduced to linear classifiers. These have parameters which weight input features, and output a scalar that is used to assign a point to a class. We learn that these… parameters classifier function loss output vector point error linear points respect bias parameter label hair toys times weight gradient minus From Elliot Crowley on November 5th, 2020 0 likes 11 plays 0
06:48 ML4: An introduction to Classification ML4: An introduction to Classification	ML4: An introduction to Classification Here, we explain what is meant by a (binary) classifier. It is a black box that takes in some data and predicts whether it belongs to one of two possible classes. We also describe how we can split… classifier spam dog training test machine cat cats dogs examples algorithms emails classes things accuracy dataset algorithm reason musk elon From Elliot Crowley on November 5th, 2020 0 likes 8 plays 0
01:59 Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction	Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction iaml pca space classification bayes dimensionality dimensions lot reason harm classes goal sets algorithms class downside upside assumption independence classifier classifiers From Nigel Goddard on September 25th, 2016 1 likes 2,135 plays 0 Licence Type Creative Commons - Attribution
03:00 Linear discriminant analysis Linear discriminant analysis	Linear discriminant analysis Linear discriminant analysis iaml pca class projection lda means dimension classifier variance direction counterexamples variances difference accuracy classification reds greens problem linear threshold hand From Nigel Goddard on September 25th, 2016 1 likes 1,636 plays 0 Licence Type Creative Commons - Attribution
01:52 Classification with PCA features Classification with PCA features	Classification with PCA features Classification with PCA features iaml pca dimension points class variance classes classifier swamps slide space colors point projection alright distribution dimensions coordinates decomposition labels redundancy From Nigel Goddard on September 25th, 2016 1 likes 1,728 plays 0 Licence Type Creative Commons - Attribution
02:21 Feature selection and feature extraction Feature selection and feature extraction	Feature selection and feature extraction Feature selection and feature extraction iaml pca attributes feature structure ways extraction variables combination combinations classifier x3 x2 selection things goal vm function d. inputs subset left From Nigel Goddard on September 25th, 2016 0 likes 2,107 plays 0 Licence Type Creative Commons - Attribution
06:39 Evaluating clustering systems Evaluating clustering systems	Evaluating clustering systems Evaluating clustering systems iaml k-means clustering task cluster clusters classification images algorithm pairs classifier evaluation classes bit groups question centroids variance distance judgments bunch bitmaps From Nigel Goddard on September 25th, 2016 1 likes 2,081 plays 0 Licence Type Creative Commons - Attribution
01:58 Sensitivity to outliers Sensitivity to outliers	Sensitivity to outliers Sensitivity to outliers iaml nearest neighbours neighbor point change neighbors class classifier blue lot notion training space region boundary decision problems stability bit neighbour vote majority From Nigel Goddard on September 21st, 2016 1 likes 1,817 plays 0 Licence Type Creative Commons - Attribution
03:29 Number of nearest neighbors to use Number of nearest neighbors to use	Number of nearest neighbors to use Number of nearest neighbors to use iaml nearest neighbours validation training class dataset values performance knn label middle classifier boundaries decision classifications algorithm procedure examples moment error setting equals From Nigel Goddard on September 21st, 2016 1 likes 1,939 plays 0 Licence Type Creative Commons - Attribution No Derivatives
03:43 Pros and cons of decision trees Pros and cons of decision trees	Pros and cons of decision trees Pros and cons of decision trees classification decision tree attribute philosophical terminology number yellow impossible decision theory caption complete compact boundaries settle split pretty approach iaml handle separate trees optical spectrum reason produce greedy decide decision trees algorithm thought depth continue representation problem classifier effects tree decision attributes splits lot cut blue access classifiers linear choice From Nigel Goddard on September 21st, 2016 0 likes 4,091 plays 0 Licence Type Creative Commons - Attribution
01:48 Decision Trees are DNF Formulas Decision Trees are DNF Formulas	Decision Trees are DNF Formulas Decision Trees are DNF Formulas iaml decision trees john tree decision rules classifier algorithm representation magic dnf function form formula logic clustering rain array head praise alright watson From Nigel Goddard on September 21st, 2016 0 likes 2,547 plays 0 Licence Type Creative Commons - Attribution

Search for tag: "classifier"

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