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get sklearn.LabelEncoder() mappings after fit_transform


By : Max
Date : October 16 2020, 08:10 PM
this one helps. You should refrain from in-line initialisation if you want to be able to make use of the mappings or inverse-transformation later.
code :
data = ['A', 'A', 'B', 'C', 'B', 'B'] # `y`

le = LabelEncoder()
mapped = le.fit_transform(data)

mapping = dict(zip(le.classes_, range(1, len(le.classes_)+1)))
print(mapping)
# {'A': 1, 'B': 2, 'C': 3}
print(le.inverse_transform(mapped))
# ['A' 'A' 'B' 'C' 'B' 'B']


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LabelEncoder().fit_transform vs. pd.get_dummies for categorical coding


By : Kelly
Date : March 29 2020, 07:55 AM
Hope that helps Yes, you can skip the use of LabelEncoder if you only want to encode string features. On the other hand if you have a categorical column of integers (instead of strings) then pd.get_dummies will leave as it is (see your A or C column for example). In that case you should use OneHotEncoder. Ideally OneHotEncoder would support both integer and strings but this is being worked on at the moment.

Error when trying to use labelEncoder() in sklearn "Attribute error: module object has no attribute labelEncoder&qu


By : David Masi
Date : March 29 2020, 07:55 AM
To fix the issue you can do I am new to python and sklearn. I installed sklearn using , Try this
code :
from sklearn import preprocessing
le = preprocessing.LabelEncoder()

Scikit-learn labelencoder: how to preserve mappings between batches?


By : Sliicezr
Date : March 29 2020, 07:55 AM
like below fixes the issue I suggest using Pandas' get_dummies() for this, since sklearn's OneHotEncoder() needs to see all possible categorical values when .fit(), otherwise it will throw an error when it encounters a new one during .transform().
code :
# Create toy dataset and split to batches
data_column = pd.Series(['Paris', 'Tokyo', 'Rome', 'London', 'Chicago', 'Paris'])
batch_1 = data_column[:3]
batch_2 = data_column[3:]

# Convert categorical feature column to matrix of dummy variables
batch_1_encoded = pd.get_dummies(batch_1, prefix='City')
batch_2_encoded = pd.get_dummies(batch_2, prefix='City')

# Row-bind (append) Encoded Data Back Together
final_encoded = pd.concat([batch_1_encoded, batch_2_encoded], axis=0)

# Final wrap-up. Replace nans with 0, and convert flags from float to int
final_encoded = final_encoded.fillna(0)
final_encoded[final_encoded.columns] = final_encoded[final_encoded.columns].astype(int)

final_encoded
   City_Chicago  City_London  City_Paris  City_Rome  City_Tokyo
0             0            0           1          0           0
1             0            0           0          0           1
2             0            0           0          1           0
3             0            1           0          0           0
4             1            0           0          0           0
5             0            0           1          0           0

type error with LabelEncoder.fit_transform()


By : RoOLa َ
Date : March 29 2020, 07:55 AM
To fix the issue you can do Try using following link to sklearn. LabelEncoder is a utility class and need to create the object with LabelEncoder():
code :
le = LabelEncoder()
df['Sex'] = le.fit_transform(df['Sex'])
# create test series
gender = pd.Series(['male', 'female', 'male'])

le = LabelEncoder()
transformed_val = le.fit_transform(gender)

# checking result after using label encoder
print(transformed_val)
[1 0 1]

See the mappings of a LabelEncoder


By : SBarthwal
Date : March 29 2020, 07:55 AM
hop of those help? I face a problem concerning the LabelEncoder. I applied it to a data set as follows: , I think this simple piece of code:
code :
data = ['apple', 'banana', 'kiwi', 'apple']
le = LabelEncoder()
le.fit(data)
le.classes_
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