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<p align="center"> Table 4</p>
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* **Complete Dominance** - One predictor is said to completely dominate another predictor if its dominance holds across all possible subset models (that do not include the two predictors under comparison). Back to the four-predictor model, for example, complete dominance of X<sub>1</sub> over X<sub>2</sub> is achieved if the additional R<sup>2</sup> contribution of X<sub>1</sub> is more than that of X<sub>2</sub> to the null model, the model consisting of X<sub>3</sub>, the model consisting of X<sub>4</sub>, and the model consisting of both X<sub>3</sub> and X<sub>4</sub>. In Table 3, we can see that incremental R<sup>2</sup> of X<sub>1</sub> is greater than that of X<sub>2</sub> for all subset models and hence X<sub>1</sub> completely dominates X<sub>2</sub>.
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The library can be used in combination with Principal Component Analysis (PCA) or Factor Analysis or any other feature reduction algorithm for getting accurate and intuitive importance of predictors. The purpose of determining predictor importance in the context of Dominance Analysis is not model selection but rather uncovering the individual contributions of the predictors.
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* **Complete Dominance** - One predictor is said to completely dominate another predictor if its dominance holds across all possible subset models (that do not include the two predictors under comparison). Back to the four-predictor model, for example, complete dominance of X<sub>1</sub> over X<sub>2</sub> is achieved if the additional R<sup>2</sup> contribution of X<sub>1</sub> is more than that of X<sub>2</sub> to the null model, the model consisting of X<sub>3</sub>, the model consisting of X<sub>4</sub>, and the model consisting of both X<sub>3</sub> and X<sub>4</sub>. In Table 3, we can see that incremental R<sup>2</sup> of X<sub>1</sub> is greater than that of X<sub>2</sub> for all subset models and hence X<sub>1</sub> completely dominates X<sub>2</sub>.
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\item which aspects of a service influence how likely a customer would recommend a company to others.
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* **Complete Dominance** - One predictor is said to completely dominate another predictor if its dominance holds across all possible subset models (that do not include the two predictors under comparison). Back to the four-predictor model, for example, complete dominance of X<sub>1</sub> over X<sub>2</sub> is achieved if the additional R<sup>2</sup> contribution of X<sub>1</sub> is more than that of X<sub>2</sub> to the null model, the model consisting of X<sub>3</sub>, the model consisting of X<sub>4</sub>, and the model consisting of both X<sub>3</sub> and X<sub>4</sub>. In Table 3, we can see that incremental R<sup>2</sup> of X<sub>1</sub> is greater than that of X<sub>2</sub> for all subset models and hence X<sub>1</sub> completely dominates X<sub>2</sub>.
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For each predictor variable, the library lists out all the predictors that are dominated generally, conditionally, and completely by it.
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**Getting R<sup>2</sup> of the Complete Model**
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<img src='images/WaterFall_Boston_cov.png'>
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7. Luo, W., & Azen, R. (2013). Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis. Journal of Educational and Behavioral Statistics, 38(1), 3-31. https://doi.org/10.3102/1076998612458319
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Dominance Levels
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This package is designed to get an intuitive and accurate global explanation to Machine Learning Models by determining the relative importance of predictors in the dataset. The package supports classification and regression models. The determination of relative importance depends on how one defines importance. Budescu et.al [@budescu:1993; @Razia:2001; @Razia:2003] proposed using dominance analysis (DA) because it invokes a general and intuitive definition of "relative importance" that is based on the additional contribution of a predictor in all subset models. The purpose of determining predictor importance in the context of DA is not model selection but rather uncovering the individual contributions of the predictors.
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Let's consider a scenario where we have 4 predictors; $X_1$, $X_2$, $X_3$ and $X_4$. We will have to build a total of $2^4$-1 models i.e. 15 models - $^4C_1$ = 4 models with only one predictor, $^4C_2$ = 6 models with two predictors each, $^4C_3$ = 4 models with three predictors each and 1 ($^4C_4$) complete model with all 4 predictors. Thus, the additional contributions of $X_1$ are computed as the increases in the proportion of variance accounted for when $X_1$ is added to each subset of the remaining predictors (i.e., the null subset $\{.\}$, $\{X_2\}$, $\{X_3\}$, $\{X_4\}$, $\{X_2X_3\}$, $\{X_2X_4\}$, $\{X_3X_4\}$ and $\{X_2X_3X_4\}$). Similarly, the additional contributions of $X_2$ are the increases in the proportion of variance accounted for when $X_2$ is added to each subset of the remaining predictors (i.e., the null subset $\{.\}$, $\{X_1\}$, $\{X_3\}$, $\{X_4\}$, $\{X_1X_3\}$, $\{X_1X_4\}$, $\{X_3X_4\}$ and $\{X_1X_3X_4\}$).
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The \autoref{fig:formulas} is the illustration of formulas used to compute the averaged additional contributions of $X_1$ and $X_2$ within model size in the population with four predictors (we use the notation $\rho^2_{Y.X}$ to represent the proportion of variance in $Y$ that is accounted for by the predictors in the model $X$. For example, $\rho^2_{Y.X_1X_3}$ represents the proportion of variance in $Y$ that is accounted for by the model consisting of $X_1$ and $X_3$. The additional contribution of a given predictor is measured by the increase in the proportion of variance that results from adding that predictor to the regression model):
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1. **Complete Dominance:** A predictor is said to completely dominate another predictor if its dominance holds across all possible subset models (that do not include the two predictors under comparison). For example, in a four-predictor model, $X_1$ is said to have complete dominance over $X_2$ when the additional R-square contribution of $X_1$ is more than that of $X_2$ for all subset models i.e. to the null model, the model consisting of $X_3$, the model consisting of $X_4$, and the model consisting of both $X_3$ and $X_4$.
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MD013 - Line length
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You also have the option to exclude this rule for code blocks and tables. To
do this, set the code_blocks
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parameters to false.
Code blocks are included in this rule by default since it is often a requirement for document readability, and tentatively compatible with code rules. Still, some languages do not lend themselves to short lines.