levels of predictive performance than the elastic net in most cases (average AUC elastic net = 70%, AUC random forest = 75%), suggesting that the features are likely to interact with one another in predicting student retention and might not always be linearly related to the outcome....
The name or data type of the column doesn't matter, as long as the column contains a single outcome (or dependent variable) that you're trying to predict. If you aren't sure which column has the label, look for a generic name such as Class or Target. If the dataset doesn't ...
With the data containers ready, I need to go over the games, select the skills of the players in each game, add some noise to them, and compare the performances to generate the game outcome: C# Copy var players1 = Variable.Array<int>(game); var players2 = Variable.Array<int>(game...
A learner interviews a peer in another country/region about the local weather. This is a task that learners conduct together, but they don't have mutual responsibility for its outcome. The following scenarios meet the requirement for shared responsibility: ...
ContrastiveExplanation (Foil Trees) "provides an explanation for why an instance had the current outcome (fact) rather than a targeted outcome of interest (foil). These counterfactual explanations limit the explanation to the features relevant in distinguishing fact from foil, thereby disregarding irrele...
IS methods aggregate the outcome of single-instance classifiers applied to the instances of a bag, whereas ES methods map the instances to a vector, followed by use of a single-instance classifier. In the BS paradigm, the instances are transformed to a non-vectorial space where the ...
Step 5: Calculate the distance between each alternative and the fuzzy positive ideal outcome 𝐴∗A∗ and the distance between each alternative and the fuzzy negative ideal outcome 𝐴−A−. The gap between each alternative and FPIS and the gap between each alternative and FNIS are calcu...
Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining...
At the core of many data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the causal effect of an intervention on an outcome of interest for a sample with a particular set of features? In a nutshell, this toolkit is designed to measure the ...
The outcome is presented by a convolution (“∗” symbol) of the driver with the impulse-response function (IRF), describing the impulse response flowing through time as shown in Equation (1). 𝑆𝐶=𝑆𝐶𝐷𝑟𝑖𝑣𝑒𝑟∗𝐼𝑅𝐹SC=SCDriver∗IRF (1) The 𝑆𝐶SC...