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nidhal baccouri
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Nov 19, 2021
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dataset: | ||
type: csv | ||
random_numbers: | ||
generate_reproducible: true | ||
seed: 101 | ||
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# model definition | ||
model: | ||
type: classification | ||
algorithm: NeuralNetwork | ||
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# target you want to predict | ||
# target(s) you want to predict | ||
target: | ||
- sick |
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# dataset operations | ||
dataset: | ||
type: csv | ||
random_numbers: | ||
generate_reproducible: true | ||
seed: 42 | ||
split: # split options | ||
test_size: 0.2 # 0.2 means 20% for the test data, so 80% are automatically for training | ||
shuffle: True # whether to shuffle the data before/while splitting | ||
split: | ||
test_size: 0.2 | ||
shuffle: True | ||
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preprocess: # preprocessing options | ||
missing_values: mean # other possible values: [drop, median, most_frequent, constant] check the docs for more | ||
preprocess: | ||
missing_values: mean | ||
encoding: | ||
type: oneHotEncoding # other possible values: [labelEncoding] | ||
scale: # scaling options | ||
method: standard # standardization will scale values to have a 0 mean and 1 standard deviation | you can also try minmax | ||
target: inputs # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled | ||
type: oneHotEncoding | ||
scale: | ||
method: standard | ||
target: inputs | ||
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# model definition | ||
model: | ||
type: classification | ||
algorithm: RandomForest | ||
arguments: | ||
n_estimators: 100 | ||
max_depth: 30 | ||
hyperparameter_search: | ||
method: random_search | ||
parameter_grid: | ||
max_depth: [6, 10] | ||
n_estimators: [100, 300] | ||
max_features: [auto, sqrt] | ||
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# target you want to predict | ||
target: | ||
- sick |
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[bumpversion] | ||
current_version = 0.1.1 | ||
current_version = 0.7.0 | ||
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[darglint] | ||
strictness = long | ||
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