Datasets:

criteo
/

CriteoClickLogs

@@ -10,18 +10,6 @@ size_categories:
 - n>1T
 ---
----
-license: cc-by-nc-sa-4.0
-task_categories:
-- text-classification
-tags:
-- commerce
-- advertising
-- click
-size_categories:
-- n>1T
----
 # 📊 Criteo 1TB Click Logs Dataset
 This dataset contains **feature values and click feedback** for millions of display ads. Its primary purpose is to **benchmark algorithms for clickthrough rate (CTR) prediction**.
@@ -29,7 +17,6 @@ This dataset contains **feature values and click feedback** for millions of disp
 It is similar, but larger than the dataset released for the Display Advertising Challenge hosted by Kaggle:
 🔗 [Kaggle Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge)
----
 ## 📁 Full Description
@@ -42,8 +29,6 @@ This dataset contains **24 files**, each corresponding to **one day of data**.
 - The **first column** indicates whether the ad was **clicked (1)** or **not clicked (0)**.
 - Both **positive (clicked)** and **negative (non-clicked)** examples have been **subsampled**, though at **different rates** to keep business confidentiality.
----
 ## 🧱 Features
 - **13 integer features**
@@ -55,16 +40,14 @@ This dataset contains **24 files**, each corresponding to **one day of data**.
 > ⚠️ Some features may contain **missing values**.
----
 ## 🧾 Data Format
 - Rows are **chronologically ordered**
 - Columns are **tab-separated** and follow this schema:
-  <label> <int_feature_1> ... <int_feature_13> <cat_feature_1> ... <cat_feature_26>
 - If a value is missing, the field is simply **left empty**.
----
 ## 🔄 Differences from Kaggle Challenge Dataset
@@ -72,5 +55,4 @@ This dataset contains **24 files**, each corresponding to **one day of data**.
 - 🔄 **Subsampling ratios** differ
 - 🔢 **Ordering of features** is different
 - 🧮 Some features have **different computation methods**
-- 🔐 **Hash function** for categorical features has changed

 - n>1T
 ---
 # 📊 Criteo 1TB Click Logs Dataset
 This dataset contains **feature values and click feedback** for millions of display ads. Its primary purpose is to **benchmark algorithms for clickthrough rate (CTR) prediction**.
 It is similar, but larger than the dataset released for the Display Advertising Challenge hosted by Kaggle:
 🔗 [Kaggle Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge)
 ## 📁 Full Description
 - The **first column** indicates whether the ad was **clicked (1)** or **not clicked (0)**.
 - Both **positive (clicked)** and **negative (non-clicked)** examples have been **subsampled**, though at **different rates** to keep business confidentiality.
 ## 🧱 Features
 - **13 integer features**
 > ⚠️ Some features may contain **missing values**.
 ## 🧾 Data Format
 - Rows are **chronologically ordered**
 - Columns are **tab-separated** and follow this schema:
+  > '''<label> <int_feature_1> ... <int_feature_13> <cat_feature_1> ... <cat_feature_26>'''
 - If a value is missing, the field is simply **left empty**.
 ## 🔄 Differences from Kaggle Challenge Dataset
 - 🔄 **Subsampling ratios** differ
 - 🔢 **Ordering of features** is different
 - 🧮 Some features have **different computation methods**
+- 🔐 **Hash function** for categorical features has changed