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In LIME, what is the purpose of perturbing the dataset?

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שאלה #132100

		To introduce randomness into the model predictions
		To generate additional data points for training
done		To modify certain interpretable components of the original data
		To eliminate outliers from the dataset

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Which type of model is typically trained in the LIME framework to approximate the behavior of the black box model?

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שאלה #132101

		Deep neural network
		Support vector machine
		Random forest
done		Simple linear model

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What role do weights play in the LIME process?

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שאלה #132102

		They determine the learning rate of the surrogate model
done		They assign importance to different features in the dataset
		They control the level of regularization applied to the model
		They measure the uncertainty of the model predictions

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What is the key advantage of LIME's model-agnostic nature?

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שאלה #132103

done		It enables LIME to provide explanations for any type of machine learning model
		It allows LIME to train models faster than other interpretability techniques
		It ensures that LIME's explanations are always accurate and reliable
		It limits the scope of LIME's applicability to specific model architectures

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In a waterfall plot in SHAP, what does f(x) represents?

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שאלה #132106

sentiment_very_satisfied		the predicted value of the model
		the expected value of the target variable, or in other words, the mean of all predictions

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In a waterfall plot in SHAP, what does E[X] represents?

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שאלה #132107

		the predicted value of the model
done		the expected value of the target variable, or in other words, the mean of all predictions

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What does SHAP stand for in the context of machine learning explanations?

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שאלה #132111

		Supervised Hierarchical Analysis Procedure
done		Shapley Additive Explanations
		Structured Hierarchical Attribution Protocol
		Statistical Hypothesis and Prediction

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What is the fundamental idea behind SHAP's approach to explanation?

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שאלה #132112

done		To quantify the marginal contributions of individual features to predictions
		To simplify complex models into interpretable representations
		To provide explanations for the entire dataset
		To visualize the decision boundaries of machine learning models

נערך לפני 6 חודשים Shachar Adam

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How does SHAP compute feature contributions to individual predictions?

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שאלה #132113

		By calculating the gradient of the loss function
		By performing principal component analysis
done		By analyzing differences in predictions across subsets of features
		By applying clustering algorithms to the input data

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What challenge does SHAP face in large-scale datasets with numerous input features?

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שאלה #132114

		Lack of interpretability
		Overfitting of models
		Data imbalance issues
done		Complexity of calculations

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