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update notebook, sklearn parameter rename #465

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update notebook, sklearn parameter rename #465

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2 changes: 1 addition & 1 deletion learntools/ml_intermediate/ex3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,7 +125,7 @@ class OneHot(CodingProblem):
"`X_valid[low_cardinality_cols]`, respectively.")
_solution = CS(
"""# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))

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4 changes: 2 additions & 2 deletions learntools/time_series/ex3.py
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Expand Up @@ -118,7 +118,7 @@ class Q4(EqualityCheckProblem): # Create holiday features
```python
from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder(sparse=False)
ohe = OneHotEncoder(sparse_output=False)

X_holidays = pd.DataFrame(
____,
Expand All @@ -134,7 +134,7 @@ class Q4(EqualityCheckProblem): # Create holiday features
# Scikit-learn solution
from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder(sparse=False)
ohe = OneHotEncoder(sparse_output=False)

X_holidays = pd.DataFrame(
ohe.fit_transform(holidays),
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2 changes: 1 addition & 1 deletion notebooks/deep_learning_intro/raw/ex3.ipynb
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Expand Up @@ -63,7 +63,7 @@
"preprocessor = make_column_transformer(\n",
" (StandardScaler(),\n",
" make_column_selector(dtype_include=np.number)),\n",
" (OneHotEncoder(sparse=False),\n",
" (OneHotEncoder(sparse_output=False),\n",
" make_column_selector(dtype_include=object)),\n",
")\n",
"\n",
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2 changes: 1 addition & 1 deletion notebooks/ml_intermediate/raw/ex3.ipynb
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Expand Up @@ -538,7 +538,7 @@
"source": [
"#%%RM_IF(PROD)%%\n",
"# Apply one-hot encoder to each column with categorical data\n",
"OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
"OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)\n",
"OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[low_cardinality_cols]))\n",
"OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[low_cardinality_cols]))\n",
"\n",
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2 changes: 1 addition & 1 deletion notebooks/ml_intermediate/raw/tut3.ipynb
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Expand Up @@ -238,7 +238,7 @@
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"# Apply one-hot encoder to each column with categorical data\n",
"OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
"OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)\n",
"OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))\n",
"OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))\n",
"\n",
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2 changes: 1 addition & 1 deletion notebooks/time_series/raw/ex3.ipynb
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Expand Up @@ -513,7 +513,7 @@
"# Scikit-learn solution\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"ohe = OneHotEncoder(sparse=False)\n",
"ohe = OneHotEncoder(sparse_output=False)\n",
"\n",
"X_holidays = pd.DataFrame(\n",
" ohe.fit_transform(holidays),\n",
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