@@ -26,8 +26,6 @@ You can find our paper `Mambular: A Sequential Model for Tabular Deep Learning`
2626 - [ Features] ( #features )
2727 - [ Models] ( #models )
2828 - [ Results] ( #results )
29- - [ Regression Results] ( #regression-results )
30- - [ Classification Results] ( #classification-results )
3129 - [ Documentation] ( #documentation )
3230 - [ Installation] ( #installation )
3331- [ Usage] ( #usage )
@@ -83,49 +81,53 @@ The average rank table over all models and all datasets is given here:
8381
8482<div align =" center " >
8583
86- | ** Model** | ** Avg. Rank** |
87- | -------------------- | --------------------------- |
88- | ** Mambular** | ** 2.083** <sub >±1.037</sub > |
89- | FT-Transformer | 2.417 <sub >±1.256</sub > |
90- | XGBoost | 3.167 <sub >±2.577</sub > |
91- | MambaTab* | 4.333 <sub >±1.374</sub > |
92- | ResNet | 4.750 <sub >±1.639</sub > |
93- | TabTransformer | 6.222 <sub >±1.618</sub > |
94- | MLP | 6.500 <sub >±1.500</sub > |
95- | MambaTab | 6.583 <sub >±1.801</sub > |
96- | MambaTab<sup >T</sup > | 7.917 <sub >±1.187</sub > |
84+ <table >
85+ <tr >
86+ <th style="text-align:center;">Model</th>
87+ <th style="text-align:center;">Avg. Rank</th>
88+ </tr >
89+ <tr >
90+ <td style="text-align:center;"><strong style="color:#2e8b57;">Mambular</strong><br><br></td>
91+ <td style="text-align:center;"><strong style="color:#2e8b57;">2.083</strong> <sub>±1.037</sub></td>
92+ </tr >
93+ <tr >
94+ <td style="text-align:center;">FT-Transformer</td>
95+ <td style="text-align:center;">2.417 <sub>±1.256</sub></td>
96+ </tr >
97+ <tr >
98+ <td style="text-align:center;">XGBoost</td>
99+ <td style="text-align:center;">3.167 <sub>±2.577</sub></td>
100+ </tr >
101+ <tr >
102+ <td style="text-align:center;">MambaTab*</td>
103+ <td style="text-align:center;">4.333 <sub>±1.374</sub></td>
104+ </tr >
105+ <tr >
106+ <td style="text-align:center;">ResNet</td>
107+ <td style="text-align:center;">4.750 <sub>±1.639</sub></td>
108+ </tr >
109+ <tr >
110+ <td style="text-align:center;">TabTransformer</td>
111+ <td style="text-align:center;">6.222 <sub>±1.618</sub></td>
112+ </tr >
113+ <tr >
114+ <td style="text-align:center;">MLP</td>
115+ <td style="text-align:center;">6.500 <sub>±1.500</sub></td>
116+ </tr >
117+ <tr >
118+ <td style="text-align:center;">MambaTab</td>
119+ <td style="text-align:center;">6.583 <sub>±1.801</sub></td>
120+ </tr >
121+ <tr >
122+ <td style="text-align:center;">MambaTab<sup>T</sup></td>
123+ <td style="text-align:center;">7.917 <sub>±1.187</sub></td>
124+ </tr >
125+ </table >
97126
98127</div >
99128
100129
101- The detailed results for different datasets are given below.
102130
103- #### Regression Results
104- The given values are MSE values. All results are achieved with 5-fold CV and the standard deviation is reported.
105-
106-
107- | Models | DI ↓ | AB ↓ | CA ↓ | WI ↓ | PA ↓ | HS ↓ | CP ↓ |
108- | -------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- |
109- | XGBoost | 0.019 <sub >±0.000</sub > | 0.506 <sub >±0.044</sub > | 0.171 <sub >±0.007</sub > | ** 0.528** <sub >±0.008</sub > | 0.036 <sub >±0.004</sub > | 0.119 <sub >±0.024</sub > | ** 0.024** <sub >±0.004</sub > |
110- | FT-Transformer | 0.018 <sub >±0.001</sub > | 0.458 <sub >±0.055</sub > | 0.169 <sub >±0.006</sub > | 0.615 <sub >±0.012</sub > | ** 0.024** <sub >±0.005</sub > | ** 0.111** <sub >±0.014</sub > | 0.024 <sub >±0.001</sub > |
111- | MLP | 0.066 <sub >±0.003</sub > | 0.462 <sub >±0.051</sub > | 0.198 <sub >±0.011</sub > | 0.654 <sub >±0.013</sub > | 0.764 <sub >±0.023</sub > | 0.147 <sub >±0.017</sub > | 0.031 <sub >±0.001</sub > |
112- | TabTransformer | 0.065 <sub >±0.002</sub > | 0.472 <sub >±0.057</sub > | 0.247 <sub >±0.013</sub > | - | 0.135 <sub >±0.001</sub > | 0.160 <sub >±0.028</sub > | - |
113- | ResNet | 0.039 <sub >±0.018</sub > | 0.455 <sub >±0.045</sub > | 0.178 <sub >±0.006</sub > | 0.639 <sub >±0.013</sub > | 0.606 <sub >±0.031</sub > | 0.141 <sub >±0.017</sub > | 0.030 <sub >±0.002</sub > |
114- | MambaTab* | 0.040 <sub >±0.008</sub > | 0.455 <sub >±0.043</sub > | 0.180 <sub >±0.008</sub > | 0.601 <sub >±0.010</sub > | 0.571 <sub >±0.021</sub > | 0.122 <sub >±0.017</sub > | 0.030 <sub >±0.002</sub > |
115- | ** Mambular** | ** 0.018** <sub >±0.000</sub > | ** 0.452** <sub >±0.043</sub > | ** 0.167** <sub >±0.011</sub > | 0.628 <sub >±0.010</sub > | 0.035 <sub >±0.005</sub > | 0.132 <sub >±0.020</sub > | 0.027 <sub >±0.002</sub > |
116-
117- #### Classification Results
118- The table reports AUC values for binary classification tasks
119-
120- | Models | BA ↑ | AD ↑ | CH ↑ | FI ↑ | MA ↑ |
121- | -------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- | --------------------------- |
122- | XGBoost | ** 0.928** <sub >±0.004</sub > | ** 0.929** <sub >±0.002</sub > | 0.845 <sub >±0.008</sub > | 0.774 <sub >±0.009</sub > | ** 0.922** <sub >±0.002</sub > |
123- | FT-Transformer | 0.926 <sub >±0.003</sub > | 0.926 <sub >±0.002</sub > | ** 0.863** <sub >±0.007</sub > | 0.792 <sub >±0.011</sub > | 0.916 <sub >±0.003</sub > |
124- | MLP | 0.895 <sub >±0.004</sub > | 0.914 <sub >±0.002</sub > | 0.840 <sub >±0.005</sub > | 0.793 <sub >±0.011</sub > | 0.886 <sub >±0.003</sub > |
125- | TabTransformer | 0.921 <sub >±0.004</sub > | 0.912 <sub >±0.002</sub > | 0.835 <sub >±0.007</sub > | - | 0.910 <sub >±0.002</sub > |
126- | ResNet | 0.896 <sub >±0.006</sub > | 0.917 <sub >±0.002</sub > | 0.841 <sub >±0.006</sub > | 0.793 <sub >±0.013</sub > | 0.889 <sub >±0.003</sub > |
127- | MambaTab* | 0.900 <sub >±0.004</sub > | 0.916 <sub >±0.003</sub > | 0.846 <sub >±0.007</sub > | 0.792 <sub >±0.011</sub > | 0.890 <sub >±0.003</sub > |
128- | ** Mambular** | 0.927 <sub >±0.006</sub > | 0.928 <sub >±0.002</sub > | 0.856 <sub >±0.004</sub > | ** 0.795** <sub >±0.011</sub > | 0.917 <sub >±0.003</sub > |
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130132
131133## Documentation
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