Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models An explainable model to support the decision about the therapy protocol for AML Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind...
To simulate the reward-oriented model we used a q-learning algorithm with the group-level parameters estimated from the model-fitting procedure, with the Q values of all options initiated at the value of 50. The experimental simulations included 3 types of action patterns: Constant (a–a–a–...
PiML also works for arbitrary supervised ML models under regression and binary classification settings. It supports a whole spectrum of outcome testing, including but not limited to the following: Accuracy: popular metrics like MSE, MAE for regression tasks and ACC, AUC, Recall, Precision, F1-sco...
This is the code I have usedfrom sklearn.linear_model import LogisticRegression # Import the three supervised learning models from sklearn from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.ensemble import AdaBoost...
Kernel regression is a supervised learning problem where one estimates a function from a number of observations. For our setup, let \({\mathcal{D}}={\{{{\bf{x}}}^{\mu },{y}^{\mu }\}}_{\mu = 1}^{P}\) be a sample of P observations drawn from a probability distribution on...
AdaBoost is a boosted algorithm that is similar to Random Forests but has a couple of significant differences: Rather than a forest of trees, AdaBoost typically makes a forest of stumps (a stump is a tree with only one node and two leaves). ...
This study investigated the utility of supervised machine learning (SML) and explainable artificial intelligence (AI) techniques for modeling and understanding human decision-making during multiagent task performance. Long short-term memory (LSTM) networks were trained to predict the target selection ...
employed to model the multivariate correlation of SPTMRP profile with 15 sound features (including 5 Timbres, 4 Rhythms and 6 Pitchs) and 5 individual features in a supervised manner, which is also improved by genetic algorithm (GA) feature selection and compared with other machine learning ...
The models were fitted using the numeric BOBYQA-optimiser algorithm implemented in the R-package lme442. The specified models were then compared with likelihood ratio tests. We used the parameters obtained from the winning model to predict responses on the test set to validate the performance of ...
"Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models" (B. Lengerich, S. Tan, C. Chang, G. Hooker, R. Caruana 2019) @article{lengerich2019purifying, title={Purifying Interaction Effects with the Functional ANOVA: An Efficient Al...