various methodologies, such as filter methods that rely on specific statistical metrics, wrapper methods like backward feature elimination, or specialized algorithms including LASSO (Least Absolute Shrinkage and Selection Operator) regression59, which are instrumental in refining the model for optimal ...
which was used for approximating the solution of\(\textbf{U} = \textbf{F}\textbf{W}\)in Sect.2.2. Instead of introducing an L1-regularized problem as done in Eq.10, we can directly solve this regression problem using the normal equations ...
underscoring its importance for the success of the model. A word of caution is necessary when using data obtained through different DFT methods, as certain properties, such as band gaps, can exhibit
Theorem 2 connects Shapley values from game theory with weighted linear regression. Kernal SHAP uses this connection to compute feature importance. This leads to more accurate estimates with fewer evaluations of the original model than previous sampling-based estimates of Equation 8, particularly when r...
The main scripts are streakInterpretationExample.py and streakRegressionExample.py. The Jupyter notebook StreakImageRetraining.ipynb is also available as a convenient walkthrough of streakIntrepretationExample. tf_predict.py can also be used from the command line to load the tensorflow model and pred...
In the implementation adopted in this study, the surrogate local model corresponds to a weighted LASSO lineal regression model [56]. 3. Results 3.1. Model (Dataset)-Level Interpretation The AI/ML/DL model interpretation process begins at model performance level, “as interpretation can only be ...
timeit from tqdm.notebook import tqdm from sklearn.feature_selection import VarianceThreshold,\ mutual_info_classif, SelectKBest from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LogisticRegression,\ LassoCV, LassoLarsCV, LassoLarsIC from mlxtend.feature_selection.....