MLP

task:

Model

subtask:

regression

host:

chemml

function:

MLP

input tokens (receivers):

api : instance of chemml.nn.keras.MLP class

types: (“<class ‘chemml.nn.keras.mlp.MLP’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of chemml.nn.keras.MLP class

types: (“<class ‘chemml.nn.keras.mlp.MLP’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

ChemML, 0.4.1

keras, 2.1.2

tensorflow, 1.4.1

config file view:

##

<< host = chemml    << function = MLP

<< func_method = None

<< nhidden = 1

<< loss = mean_squared_error

<< learning_rate = 0.01

<< layer_config_file = None

<< batch_size = 100

<< lr_decay = 0.0

<< regression = True

<< nclasses = None

<< activations = None

<< opt_config_file = None

<< nepochs = 100

<< nneurons = 100

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters:

MLP_sklearn

task:

Model

subtask:

regression

host:

chemml

function:

MLP_sklearn

input tokens (receivers):

api : instance of chemml.nn.keras.MLP_sklearn class

types: (“<class ‘chemml.nn.keras.mlp.MLP_sklearn’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of chemml.nn.keras.MLP_sklearn class

types: (“<class ‘chemml.nn.keras.mlp.MLP_sklearn’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

ChemML, 0.4.1

scikit-learn, 0.19.0

keras, 2.1.2

tensorflow, 1.4.1

config file view:

##

<< host = chemml    << function = MLP_sklearn

<< func_method = None

<< nhidden = 1

<< loss = mean_squared_error

<< learning_rate = 0.01

<< layer_config_file = None

<< batch_size = 100

<< lr_decay = 0.0

<< regression = True

<< nclasses = None

<< activations = None

<< opt_config_file = None

<< nepochs = 100

<< nneurons = 100

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters:

ARDRegression

task:

Model

subtask:

regression

host:

sklearn

function:

ARDRegression

input tokens (receivers):

api : instance of scikit-learn’s ARDRegression class

types: (“<class ‘sklearn.linear_model.bayes.ARDRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s ARDRegression class

types: (“<class ‘sklearn.linear_model.bayes.ARDRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = ARDRegression

<< track_header = True

<< func_method = None

<< normalize = False

<< n_iter = 300

<< verbose = False

<< lambda_2 = 1e-06

<< fit_intercept = True

<< threshold_lambda = 10000.0

<< compute_score = False

<< alpha_2 = 1e-06

<< tol = 0.001

<< alpha_1 = 1e-06

<< copy_X = True

<< lambda_1 = 1e-06

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ARDRegression.html

BayesianRidge

task:

Model

subtask:

regression

host:

sklearn

function:

BayesianRidge

input tokens (receivers):

api : instance of scikit-learn’s BayesianRidge class

types: (“<class ‘sklearn.linear_model.bayes.BayesianRidge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s BayesianRidge class

types: (“<class ‘sklearn.linear_model.bayes.BayesianRidge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = BayesianRidge

<< track_header = True

<< func_method = None

<< normalize = False

<< n_iter = 300

<< verbose = False

<< lambda_2 = 1e-06

<< fit_intercept = True

<< compute_score = False

<< alpha_2 = 1e-06

<< tol = 0.001

<< alpha_1 = 1e-06

<< copy_X = True

<< lambda_1 = 1e-06

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html

ElasticNet

task:

Model

subtask:

regression

host:

sklearn

function:

ElasticNet

input tokens (receivers):

api : instance of scikit-learn’s ElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s ElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.ElasticNet’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = ElasticNet

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< l1_ratio = 0.5

<< max_iter = 1000

<< precompute = False

<< random_state = None

<< tol = 0.0001

<< positive = False

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html

KernelRidge

task:

Model

subtask:

regression

host:

sklearn

function:

KernelRidge

input tokens (receivers):

api : instance of scikit-learn’s KernelRidge class

types: (“<class ‘sklearn.kernel_ridge.KernelRidge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s KernelRidge class

types: (“<class ‘sklearn.kernel_ridge.KernelRidge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = KernelRidge

<< track_header = True

<< func_method = None

<< kernel = linear

<< degree = 3

<< kernel_params = None

<< alpha = 1

<< coef0 = 1

<< gamma = None

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html

Lars

task:

Model

subtask:

regression

host:

sklearn

function:

Lars

input tokens (receivers):

api : instance of scikit-learn’s Lars class

types: (“<class ‘sklearn.linear_model.least_angle.Lars’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s Lars class

types: (“<class ‘sklearn.linear_model.least_angle.Lars’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = Lars

<< track_header = True

<< func_method = None

<< n_nonzero_coefs = 500

<< normalize = True

<< fit_path = True

<< fit_intercept = True

<< positive = False

<< eps = 2.22044604925e-16

<< precompute = auto

<< copy_X = True

<< verbose = False

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html

Lasso

task:

Model

subtask:

regression

host:

sklearn

function:

Lasso

input tokens (receivers):

api : instance of scikit-learn’s Lasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s Lasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.Lasso’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = Lasso

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< positive = False

<< max_iter = 1000

<< precompute = False

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

LassoLars

task:

Model

subtask:

regression

host:

sklearn

function:

LassoLars

input tokens (receivers):

api : instance of scikit-learn’s LassoLars class

types: (“<class ‘sklearn.linear_model.least_angle.LassoLars’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s LassoLars class

types: (“<class ‘sklearn.linear_model.least_angle.LassoLars’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = LassoLars

<< track_header = True

<< func_method = None

<< normalize = True

<< fit_path = True

<< fit_intercept = True

<< positive = False

<< max_iter = 500

<< eps = 2.22044604925e-16

<< precompute = auto

<< copy_X = True

<< alpha = 1.0

<< verbose = False

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html

LinearRegression

task:

Model

subtask:

regression

host:

sklearn

function:

LinearRegression

input tokens (receivers):

api : instance of scikit-learn’s LinearRegression class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s LinearRegression class

types: (“<class ‘sklearn.linear_model.base.LinearRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = LinearRegression

<< track_header = True

<< func_method = None

<< normalize = False

<< n_jobs = 1

<< fit_intercept = True

<< copy_X = True

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

LinearSVR

task:

Model

subtask:

regression

host:

sklearn

function:

LinearSVR

input tokens (receivers):

api : instance of scikit-learn’s LinearSVR class

types: (“<class ‘sklearn.svm.classes.LinearSVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s LinearSVR class

types: (“<class ‘sklearn.svm.classes.LinearSVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = LinearSVR

<< track_header = True

<< func_method = None

<< loss = epsilon_insensitive

<< intercept_scaling = 1.0

<< fit_intercept = True

<< epsilon = 0.0

<< max_iter = 1000

<< C = 1.0

<< random_state = None

<< dual = True

<< tol = 0.0001

<< verbose = 0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html

LogisticRegression

task:

Model

subtask:

regression

host:

sklearn

function:

LogisticRegression

input tokens (receivers):

api : instance of scikit-learn’s LogisticRegression class

types: (“<class ‘sklearn.linear_model.logistic.LogisticRegression’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s LogisticRegression class

types: (“<class ‘sklearn.linear_model.logistic.LogisticRegression’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = LogisticRegression

<< track_header = True

<< func_method = None

<< warm_start = False

<< n_jobs = 1

<< intercept_scaling = 1

<< fit_intercept = True

<< max_iter = 100

<< class_weight = None

<< C = 1.0

<< penalty = l2

<< multi_class = ovr

<< random_state = None

<< dual = False

<< tol = 0.0001

<< solver = liblinear

<< verbose = 0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

MLPRegressor

task:

Model

subtask:

regression

host:

sklearn

function:

MLPRegressor

input tokens (receivers):

api : instance of scikit-learn’s MLPRegressor class

types: (“<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s MLPRegressor class

types: (“<class ‘sklearn.neural_network.multilayer_perceptron.MLPRegressor’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = MLPRegressor

<< track_header = True

<< func_method = None

<< shuffle = True

<< verbose = False

<< random_state = None

<< tol = 0.0001

<< validation_fraction = 0.1

<< learning_rate = constant

<< momentum = 0.9

<< warm_start = False

<< epsilon = 1e-08

<< activation = relu

<< max_iter = 200

<< batch_size = auto

<< alpha = 0.0001

<< early_stopping = False

<< beta_1 = 0.9

<< beta_2 = 0.999

<< nesterovs_momentum = True

<< hidden_layer_sizes = (100,)

<< solver = adam

<< power_t = 0.5

<< learning_rate_init = 0.001

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor

MultiTaskElasticNet

task:

Model

subtask:

regression

host:

sklearn

function:

MultiTaskElasticNet

input tokens (receivers):

api : instance of scikit-learn’s MultiTaskElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s MultiTaskElasticNet class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskElasticNet’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = MultiTaskElasticNet

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< l1_ratio = 0.5

<< max_iter = 1000

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html

MultiTaskLasso

task:

Model

subtask:

regression

host:

sklearn

function:

MultiTaskLasso

input tokens (receivers):

api : instance of scikit-learn’s MultiTaskLasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s MultiTaskLasso class

types: (“<class ‘sklearn.linear_model.coordinate_descent.MultiTaskLasso’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = MultiTaskLasso

<< track_header = True

<< func_method = None

<< normalize = False

<< warm_start = False

<< selection = cyclic

<< fit_intercept = True

<< max_iter = 1000

<< random_state = None

<< tol = 0.0001

<< copy_X = True

<< alpha = 1.0

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html

NuSVR

task:

Model

subtask:

regression

host:

sklearn

function:

NuSVR

input tokens (receivers):

api : instance of scikit-learn’s NuSVR class

types: (“<class ‘sklearn.svm.classes.NuSVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s NuSVR class

types: (“<class ‘sklearn.svm.classes.NuSVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = NuSVR

<< track_header = True

<< func_method = None

<< kernel = rbf

<< verbose = False

<< degree = 3

<< coef0 = 0.0

<< max_iter = -1

<< C = 1.0

<< tol = 0.001

<< cache_size = 200

<< shrinking = True

<< nu = 0.5

<< gamma = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html

Ridge

task:

Model

subtask:

regression

host:

sklearn

function:

Ridge

input tokens (receivers):

api : instance of scikit-learn’s Ridge class

types: (“<class ‘sklearn.linear_model.ridge.Ridge’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s Ridge class

types: (“<class ‘sklearn.linear_model.ridge.Ridge’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = Ridge

<< track_header = True

<< func_method = None

<< normalize = False

<< fit_intercept = True

<< max_iter = None

<< random_state = None

<< tol = 0.001

<< copy_X = True

<< alpha = 1.0

<< solver = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

SGDRegressor

task:

Model

subtask:

regression

host:

sklearn

function:

SGDRegressor

input tokens (receivers):

api : instance of scikit-learn’s SGDRegressor class

types: (“<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s SGDRegressor class

types: (“<class ‘sklearn.linear_model.stochastic_gradient.SGDRegressor’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = SGDRegressor

<< track_header = True

<< func_method = None

<< warm_start = False

<< loss = squared_loss

<< eta0 = 0.01

<< verbose = 0

<< fit_intercept = True

<< l1_ratio = 0.15

<< average = False

<< n_iter = 5

<< penalty = l2

<< power_t = 0.25

<< alpha = 0.0001

<< random_state = None

<< epsilon = 0.1

<< shuffle = True

<< learning_rate = invscaling

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html

SVR

task:

Model

subtask:

regression

host:

sklearn

function:

SVR

input tokens (receivers):

api : instance of scikit-learn’s SVR class

types: (“<class ‘sklearn.svm.classes.SVR’>”,)

dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

output tokens (senders):

api : instance of scikit-learn’s SVR class

types: (“<class ‘sklearn.svm.classes.SVR’>”,)

dfy_predict : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

wrapper parameters:

track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

choose one of: (‘fit’, ‘predict’, None)

required packages:

scikit-learn, 0.19.0

pandas, 0.20.3

config file view:

##

<< host = sklearn    << function = SVR

<< track_header = True

<< func_method = None

<< kernel = rbf

<< verbose = False

<< degree = 3

<< coef0 = 0.0

<< epsilon = 0.1

<< max_iter = -1

<< C = 1.0

<< tol = 0.001

<< cache_size = 200

<< shrinking = True

<< gamma = auto

>> id api

>> id dfy

>> id dfx

>> id api

>> id dfy_predict

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html