Update README.md

README.md

@@ -62,19 +62,19 @@ dataset = load_dataset("inria_soda/tabular-benchmark", data_files="reg_cat/house
 
 This dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below:
 
-- Heterogeneous columns. Columns should correspond to features of different nature. This excludes
+- **Heterogeneous columns**. Columns should correspond to features of different nature. This excludes
 images or signal datasets where each column corresponds to the same signal on different sensors.
-- Not high dimensional. We only keep datasets with a d/n ratio below 1/10.
-- Undocumented datasets We remove datasets where too little information is available. We did keep
+- **Not high dimensional**. We only keep datasets with a d/n ratio below 1/10.
+- **Undocumented datasets** We remove datasets where too little information is available. We did keep
 datasets with hidden column names if it was clear that the features were heterogeneous.
-- I.I.D. data. We remove stream-like datasets or time series.
-Real-world data. We remove artificial datasets but keep some simulated datasets. The difference is
+- **I.I.D. data**. We remove stream-like datasets or time series.
+- **Real-world data**. We remove artificial datasets but keep some simulated datasets. The difference is
 subtle, but we try to keep simulated datasets if learning these datasets are of practical importance
 (like the Higgs dataset), and not just a toy example to test specific model capabilities.
-Not too small. We remove datasets with too few features (< 4) and too few samples (< 3 000). For
+- **Not too small**. We remove datasets with too few features (< 4) and too few samples (< 3 000). For
 benchmarks on numerical features only, we remove categorical features before checking if enough
 features and samples are remaining.
-- Not too easy. We remove datasets which are too easy. Specifically, we remove a dataset if a default
+- **Not too easy**. We remove datasets which are too easy. Specifically, we remove a dataset if a default
 Logistic Regression (or Linear Regression for regression) reach a score whose relative difference
 with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn) is below 5%. Other benchmarks use different metrics to
 remove too easy datasets, like removing datasets which can be learnt perfectly by a single decision
@@ -82,7 +82,7 @@ classifier [Bischl et al., 2021], but this does not account for different Bayes
 As tree-based methods have been shown to be superior to Logistic Regression [Fernández-Delgado
 et al., 2014] in our setting, a close score for these two types of models indicates that we might
 already be close to the best achievable score.
-- Not deterministic. We remove datasets where the target is a deterministic function of the data. This
+- **Not deterministic**. We remove datasets where the target is a deterministic function of the data. This
 mostly means removing datasets on games like poker and chess. Indeed, we believe that these
 datasets are very different from most real-world tabular datasets, and should be studied separately