Styjun

What happens when a metallic dragon and a chromatic dragon mate?

Is it possible to make sharp wind that can cut stuff from afar?

How did the USSR manage to innovate in an environment characterized by government censorship and high bureaucracy?

Landlord wants to switch my lease to a "Land contract" to "get back at the city"

Doomsday-clock for my fantasy planet

What does "enim et" mean?

"My colleague's body is amazing"

Unbreakable Formation vs. Cry of the Carnarium

Add an angle to a sphere

What is the offset in a seaplane's hull?

Is every set a filtered colimit of finite sets?

Does the average primeness of natural numbers tend to zero?

Manga about a female worker who got dragged into another world together with this high school girl and she was just told she's not needed anymore

Is "plugging out" electronic devices an American expression?

OA final episode explanation

Denied boarding due to overcrowding, Sparpreis ticket. What are my rights?

Is domain driven design an anti-SQL pattern?

Calculate Levenshtein distance between two strings in Python

How is it possible for user's password to be changed after storage was encrypted? (on OS X, Android)

Is there a way to make member function NOT callable from constructor?

Travelling to Edinburgh from India

If a centaur druid Wild Shapes into a Giant Elk, do their Charge features stack?

Shall I use personal or official e-mail account when registering to external websites for work purpose?

Pristine Bit Checking

How do I print the best score and the optimal values from the Hyperparameter Tuning for SGD Regressor (sklearn)?

How to loop sklearn linear regression by values within a column - pythonHow do sklearn SGDClassifier model thresholds relate to model scores?Predict different values from same inputs in a linear regressor model?How to simulate data in R, such that p-value of regressor is exactly 0.05?Predictive Maintenance - How to use Bayesian Optimization with objective function and Logistic Regression with Gradient Descent together?Why is random_state required for ridge & lasso regression classifiers?Optimal independent variable values after fitting random forest regressorHow to print regression equation from coefficients matrix?multiple output prediction from sklearn sgd classifer?Pytorch: How does SGD with momentum works when optimizer has to call zero_grad() to help accumulation of gradients?

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;

0

using Hyperparameter Tuning for SGD Regressor, I want to tune the following hyper-parameters using the following values.

alpha: 0.1, 0.01, 0.001

learning_rate: "constant", "optimal"

l1_ratio': from 0 to 1

max_iter': try larger iterations from 20000

eta0: 0.01, 0.001

I want to report the best score (negative mean squared error) & optimal hyperparameter values.

sgd_reg = SGDRegressor(alpha=0.1, average=False, early_stopping=False,
 epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15,
 learning_rate='constant', loss='squared_loss', max_iter=10000,
 n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,
 random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
 verbose=0, warm_start=False)

print("Best Score (negative mean squared error): %f", ??)

print("Optimal Hyperparameter Values: ", ??)

linear-regression gradient-descent

share|improve this question

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

add a comment | 

0

using Hyperparameter Tuning for SGD Regressor, I want to tune the following hyper-parameters using the following values.

alpha: 0.1, 0.01, 0.001

learning_rate: "constant", "optimal"

l1_ratio': from 0 to 1

max_iter': try larger iterations from 20000

eta0: 0.01, 0.001

I want to report the best score (negative mean squared error) & optimal hyperparameter values.

sgd_reg = SGDRegressor(alpha=0.1, average=False, early_stopping=False,
 epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15,
 learning_rate='constant', loss='squared_loss', max_iter=10000,
 n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,
 random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
 verbose=0, warm_start=False)

print("Best Score (negative mean squared error): %f", ??)

print("Optimal Hyperparameter Values: ", ??)

linear-regression gradient-descent

share|improve this question

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

add a comment | 

using Hyperparameter Tuning for SGD Regressor, I want to tune the following hyper-parameters using the following values.

alpha: 0.1, 0.01, 0.001

learning_rate: "constant", "optimal"

l1_ratio': from 0 to 1

max_iter': try larger iterations from 20000

eta0: 0.01, 0.001

I want to report the best score (negative mean squared error) & optimal hyperparameter values.

sgd_reg = SGDRegressor(alpha=0.1, average=False, early_stopping=False,
 epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15,
 learning_rate='constant', loss='squared_loss', max_iter=10000,
 n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,
 random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
 verbose=0, warm_start=False)

print("Best Score (negative mean squared error): %f", ??)

print("Optimal Hyperparameter Values: ", ??)

linear-regression gradient-descent

share|improve this question

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

sgd_reg = SGDRegressor(alpha=0.1, average=False, early_stopping=False,
 epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15,
 learning_rate='constant', loss='squared_loss', max_iter=10000,
 n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,
 random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
 verbose=0, warm_start=False)

share|improve this question

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

share|improve this question

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62

asked Mar 22 at 1:42

NameIsPythonNameIsPython

62