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updating docstring for memmodel/naive_bayes #774

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Oct 25, 2023
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updating docstring for memmodel/naive_bayes #774

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88ee907
updating docstring for memmodel/naive_bayes
abhsharma2 Oct 25, 2023
9950739
updating parameters formatting
abhsharma2 Oct 25, 2023
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updating parameters formatting
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@abhsharma2
abhsharma2 committed Oct 25, 2023
commit 995073984a682d863ce64a7bbb7889f048529e6c
57 changes: 31 additions & 26 deletions verticapy/machine_learning/memmodel/naive_bayes.py
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Original file line number Diff line number Diff line change
@@ -36,50 +36,55 @@ class NaiveBayes(MulticlassClassifier):
be represented by a dictionary, which differs based
on the distribution.

For 'gaussian':
- For 'gaussian':
Key 'type' must have 'gaussian' as value.
Each of the model's classes must include a
dictionary with two keys:
sigma_sq: Square root of the standard deviation.
mu: Average.

Example: {'type': 'gaussian',
'C': {'mu': 63.9878308300395,
'sigma_sq': 7281.87598377196},
'Q': {'mu': 13.0217386792453,
'sigma_sq': 211.626862330204},
'S': {'mu': 27.6928120412844,
'sigma_sq': 1428.57067393938}}
For 'multinomial':
Example:
{'type': 'gaussian',
'C': {'mu': 63.9878308300395,
'sigma_sq': 7281.87598377196},
'Q': {'mu': 13.0217386792453,
'sigma_sq': 211.626862330204},
'S': {'mu': 27.6928120412844,
'sigma_sq': 1428.57067393938}}
- For 'multinomial':
Key 'type' must have 'multinomial' as value.
Each of the model's classes must be represented
by a key with its probability as the value.

Example: {'type': 'multinomial',
'C': 0.771666666666667,
'Q': 0.910714285714286,
'S': 0.878216123499142}
For 'bernoulli':
Example:
{'type': 'multinomial',
'C': 0.771666666666667,
'Q': 0.910714285714286,
'S': 0.878216123499142}
- For 'bernoulli':
Key 'type' must have 'bernoulli' as value.
Each of the model's classes must be represented
by a key with its probability as the value.

Example: {'type': 'bernoulli',
'C': 0.537254901960784,
'Q': 0.277777777777778,
'S': 0.324942791762014}
For 'categorical':
Example:
{'type': 'bernoulli',
'C': 0.537254901960784,
'Q': 0.277777777777778,
'S': 0.324942791762014}
- For 'categorical':
Key 'type' must have 'categorical' as value.
Each of the model's classes must include
a dictionary with all the feature categories.

Example: {'type': 'categorical',
'C': {'female': 0.407843137254902,
'male': 0.592156862745098},
'Q': {'female': 0.416666666666667,
'male': 0.583333333333333},
'S': {'female': 0.311212814645309,
'male': 0.688787185354691}}
Example:
{'type': 'categorical',
'C': {'female': 0.407843137254902,
'male': 0.592156862745098},
'Q': {'female': 0.416666666666667,
'male': 0.583333333333333},
'S': {'female': 0.311212814645309,
'male': 0.688787185354691}}

prior: ArrayLike
The model's classes probabilities.
classes: ArrayLike