lassoglm function Search Results


matlab function lassoglm  (MathWorks Inc)
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MathWorks Inc matlab function lassoglm
Matlab Function Lassoglm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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linear classifier matlab function lassoglm with l1 regularization  (MathWorks Inc)
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MathWorks Inc linear classifier matlab function lassoglm with l1 regularization
Linear Classifier Matlab Function Lassoglm With L1 Regularization, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lasso logistic regression classifier  (MathWorks Inc)
90
MathWorks Inc lasso logistic regression classifier
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Logistic Regression Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lasso logistic regression classifier - by Bioz Stars, 2026-04
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regularization function lassoglm  (MathWorks Inc)
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MathWorks Inc regularization function lassoglm
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Regularization Function Lassoglm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lasso regression  (MathWorks Inc)
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MathWorks Inc lasso regression
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Regression, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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function lassoglm  (MathWorks Inc)
90
MathWorks Inc function lassoglm
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Function Lassoglm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lasso logistic regression model  (MathWorks Inc)
90
MathWorks Inc lasso logistic regression model
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Logistic Regression Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lasso logistic regression model - by Bioz Stars, 2026-04
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lassoglm function  (MathWorks Inc)
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MathWorks Inc lassoglm function
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lassoglm Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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elastic net function lassoglm  (MathWorks Inc)
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MathWorks Inc elastic net function lassoglm
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Elastic Net Function Lassoglm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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elastic net regularization lassoglm  (MathWorks Inc)
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MathWorks Inc elastic net regularization lassoglm
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Elastic Net Regularization Lassoglm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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elastic net regression lassoglm function  (MathWorks Inc)
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MathWorks Inc elastic net regression lassoglm function
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Elastic Net Regression Lassoglm Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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elastic net regression lassoglm function - by Bioz Stars, 2026-04
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Image Search Results


A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained lasso-regularized logistic regression classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each classifier output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.

Journal: Nature Communications

Article Title: Heuristics in risky decision-making relate to preferential representation of information

doi: 10.1038/s41467-024-48547-z

Figure Lengend Snippet: A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained lasso-regularized logistic regression classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each classifier output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.

Article Snippet: Following this, data from all sensors for a given timepoint was used as training examples to train a lasso logistic regression classifier (using matlab function lassoglm).

Techniques: Activation Assay, Biomarker Discovery, Generated