def tuneBaggingRegressorNValues():
nValues = [
2**4, 2**5, 2**6, 2**7, 2**8, 2**9, 2**10, 2**11, 2**12, 2**13, 2**14,
2**15, 2**16, 2**17, 2**18, 2**19
]
tuneNValue(nValues,
classifier,
classifierName,
jsonFileNames,
dataSource='reddit')
def tuneSGDNValues():
nValues = [
2**4, 2**5, 2**6, 2**7, 2**8, 2**9, 2**10, 2**11, 2**12, 2**13, 2**14,
2**15, 2**16, 2**17, 2**18, 2**19, 2**20
]
tuneNValue(nValues,
regressor,
regressorName,
jsonFileNames,
dataSource='reddit')
def tuneRandomForestNValues():
nValues = [
2**4,
2**5,
2**6,
2**7,
2**8,
2**9,
2**10,
2**11,
2**12,
2**13,
]
tuneNValue(nValues, classifier, classifierName, jsonFileNames)
def tuneKNeighborsNValues():
nValues = [
2**4,
2**5,
2**6,
2**7,
2**8,
2**9,
2**10,
1598,
2**11,
2**12,
2**13,
]
tuneNValue(nValues, classifier, classifierName, jsonFileNames)
def tuneSGDNValues():
nValues = [
2 ** 4,
2 ** 5,
2 ** 6,
2 ** 7,
2 ** 8,
2 ** 9,
2 ** 10,
1599,
2 ** 11,
2 ** 12,
2 ** 13,
]
tuneNValue(nValues, regressor, regressorName, jsonFileNames)
def tuneRandomForestNValues():
nValues = [
2 ** 4,
2 ** 5,
2 ** 6,
2 ** 7,
2 ** 8,
2 ** 9,
2 ** 10,
2 ** 11,
2 ** 12,
2 ** 13,
2 ** 14,
2 ** 15,
2 ** 16,
2 ** 17,
2 ** 18,
2 ** 19,
2 ** 20,
]
tuneNValue(nValues, classifier, classifierName, jsonFileNames, dataSource='reddit')
def tuneKNeighborsNValues():
nValues = [
2**4,
2**5,
2**6,
2**7,
2**8,
2**9,
2**10,
2**11,
2**12,
2**13,
2**14,
2**15,
2**16,
]
tuneNValue(nValues,
classifier,
classifierName,
jsonFileNames,
dataSource='reddit')
2**8,
2**9,
2**10,
2**11,
2**12,
2**13,
2**14,
2**15,
2**16,
2**17,
2**18,
2**19,
2**20,
]
machineLearning.tuneNValue(nValues, classifier, classifierName, jsonFileNames)
# Pick a reasonable n value considering you'll be training the model a few hundred times
# We want an n with a high accuracy but low run time
decentNValue = 2**15
# ______________________________________________________
# WHAT ARE THE BEST VALUES OF EACH PARAMETER?
# ______________________________________________________
#
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
"C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
"fit_intercept": [True, False],
"solver": ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
2 ** 11,
2 ** 12,
2 ** 13,
2 ** 14,
2 ** 15,
2 ** 16,
2 ** 17,
2 ** 18,
2 ** 19,
2 ** 20,
2 ** 21,
2 ** 22,
2 ** 23,
]
tuneNValue(nValues, classifier, classifierName, jsonFileNames)
# Pick a reasonable n value considering you'll be training the model a few hundred times
# We want an n with a high accuracy but low run time
decentNValue = 30000
# ______________________________________________________
# WHAT ARE THE BEST VALUES OF EACH PARAMETER?
# ______________________________________________________
#
# This should be a list of all your parameters with a wide range of possible values
parameterGrid = {
# "n_estimators": np.arange(5, 500, 10),
# "max_depth": np.arange(1, 22, 1),
# "min_samples_split": np.arange(2, 150, 4),