R2 score: mean: 0.921; std: 0.015
+R2 score: mean: 0.923; std: 0.014
The simple pipeline applied on this complex dataset gave us very good results.
@@ -939,7 +934,7 @@ ConclusionTotal running time of the script: (1 minutes 20.731 seconds)
+Total running time of the script: (1 minutes 27.261 seconds)
diff --git a/0.1/auto_examples/02_feature_interpretation_with_gapencoder.html b/0.1/auto_examples/02_feature_interpretation_with_gapencoder.html
index 966ba5a9..79fc5bd6 100644
--- a/0.1/auto_examples/02_feature_interpretation_with_gapencoder.html
+++ b/0.1/auto_examples/02_feature_interpretation_with_gapencoder.html
@@ -850,7 +850,7 @@ ConclusionGapEncoder, and how it can be used to summarize categorical variables
as a set of latent topics.
-
Total running time of the script: (0 minutes 2.168 seconds)
+Total running time of the script: (0 minutes 2.163 seconds)
diff --git a/0.1/auto_examples/03_datetime_encoder.html b/0.1/auto_examples/03_datetime_encoder.html
index 8df0e3b8..d9d1a4ed 100644
--- a/0.1/auto_examples/03_datetime_encoder.html
+++ b/0.1/auto_examples/03_datetime_encoder.html
@@ -850,7 +850,7 @@ ConclusionTableVectorizer, which automatically recognizes
and transforms datetime columns by default.
-
Total running time of the script: (0 minutes 4.849 seconds)
+Total running time of the script: (0 minutes 5.097 seconds)
diff --git a/0.1/auto_examples/04_fuzzy_joining.html b/0.1/auto_examples/04_fuzzy_joining.html
index e16172a7..23e5cb28 100644
--- a/0.1/auto_examples/04_fuzzy_joining.html
+++ b/0.1/auto_examples/04_fuzzy_joining.html
@@ -1781,7 +1781,7 @@ Using the Best parameters: {'joiner-1__max_dist': 0.1, 'joiner-2__max_dist': 0.9, 'joiner-3__max_dist': 0.1}
-Total running time of the script: (0 minutes 22.159 seconds)
+Total running time of the script: (0 minutes 22.931 seconds)
diff --git a/0.1/auto_examples/05_deduplication.html b/0.1/auto_examples/05_deduplication.html
index 5f7095ba..d8ff18e8 100644
--- a/0.1/auto_examples/05_deduplication.html
+++ b/0.1/auto_examples/05_deduplication.html
@@ -665,7 +665,7 @@ ConclusionGapEncoder
or MinHashEncoder.
-
Total running time of the script: (0 minutes 5.151 seconds)
+Total running time of the script: (0 minutes 6.062 seconds)
diff --git a/0.1/auto_examples/06_ken_embeddings.html b/0.1/auto_examples/06_ken_embeddings.html
index 3cf91499..e4928dd6 100644
--- a/0.1/auto_examples/06_ken_embeddings.html
+++ b/0.1/auto_examples/06_ken_embeddings.html
@@ -672,7 +672,7 @@ Extracting entity embeddingsfetch_ken_table_aliases()
-{'companies', 'games', 'movies', 'all_entities', 'schools', 'albums'}
+{'companies', 'games', 'all_entities', 'schools', 'movies', 'albums'}
The games table is the most relevant to our case.
@@ -968,7 +968,7 @@
Plotting the results
It helped significantly improve the prediction score.
-Total running time of the script: (11 minutes 21.562 seconds)
+Total running time of the script: (13 minutes 4.578 seconds)
diff --git a/0.1/auto_examples/07_multiple_key_join.html b/0.1/auto_examples/07_multiple_key_join.html
index 0d26fcaf..e4bd5ab8 100644
--- a/0.1/auto_examples/07_multiple_key_join.html
+++ b/0.1/auto_examples/07_multiple_key_join.html
@@ -1514,7 +1514,7 @@ Joining: feature augmentation across tablesJoiner() transformer.
Our final cross-validated accuracy score is 0.58.
-Total running time of the script: (11 minutes 23.106 seconds)
+Total running time of the script: (13 minutes 12.928 seconds)
diff --git a/0.1/auto_examples/08_join_aggregation.html b/0.1/auto_examples/08_join_aggregation.html
index 0a97a38c..25d2cd22 100644
--- a/0.1/auto_examples/08_join_aggregation.html
+++ b/0.1/auto_examples/08_join_aggregation.html
@@ -1096,75 +1096,75 @@ Hyper-parameters tuning and cross validation"value_counts" yields an
exhaustive histogram over all the possible values of ratings
(here 10 different values, from 0.5 to 5).
-Total running time of the script: (0 minutes 18.256 seconds)
+Total running time of the script: (0 minutes 23.726 seconds)
diff --git a/0.1/auto_examples/09_interpolation_join.html b/0.1/auto_examples/09_interpolation_join.html
index 5f2b6951..17926e77 100644
--- a/0.1/auto_examples/09_interpolation_join.html
+++ b/0.1/auto_examples/09_interpolation_join.html
@@ -756,9 +756,9 @@ Joining the tablesstate
-AK -3.590085
-MT 0.062396
-WA 0.147388
-ND 1.152629
-MN 1.409494
+AK -2.597808
+WA 0.834756
+ND 0.899300
+MT 0.927627
+MN 1.461241
Name: TMAX, dtype: float64
@@ -1003,11 +1003,11 @@ Sanity checksstate
-LA 21.655593
-FL 24.662459
-HI 27.341897
-VI 29.278621
-PR 29.733622
+LA 21.441632
+FL 24.618361
+HI 28.353713
+VI 30.155955
+PR 30.581503
Name: TMAX, dtype: float64
@@ -1036,7 +1036,7 @@ ConclusionInterpolationJoiner transformer: we give it a table (the weather data) and a set of matching columns (here date, latitude, longitude) and it learns to predict the other columns’ values (such as the max daily temperature).
Then, it transforms tables by predicting values that a matching row would contain, rather than by searching for an actual match.
It is a generalization of the fuzzy_join(), as fuzzy_join() is the same thing as an InterpolationJoiner where the estimators are 1-nearest-neighbor estimators.
-
Total running time of the script: (0 minutes 5.497 seconds)
+Total running time of the script: (0 minutes 7.251 seconds)
diff --git a/0.1/auto_examples/sg_execution_times.html b/0.1/auto_examples/sg_execution_times.html
index e378e859..e3e9911a 100644
--- a/0.1/auto_examples/sg_execution_times.html
+++ b/0.1/auto_examples/sg_execution_times.html
@@ -11,10 +11,10 @@
-
+
-
+
Computation times — skrub
@@ -471,7 +471,7 @@
Computation times#
-25:03.479 total execution time for 9 files from auto_examples:
+28:51.997 total execution time for 9 files from auto_examples: