Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [parameter]

The tag has no usage guidance.

1
vote
1answer
157 views

When I include validation_data=(x_val, y_val) in model.fit_generator, should I create another test dataset for accuracy measures?

While modelling in keras, often I see the usage of validation_data=(x_val, y_val) in model.fit_generator where (x_val, y_val) ...
0
votes
0answers
77 views

Kohonen SOM parameter tuning

I am currently trying to perform clustering for anomaly detection using SOMs. I chose SOMs as I am also interested in inspecting patterns on my data. I use the kohonen package in R. My dataset is ...
-1
votes
3answers
4k views

Coefficients from Logistic Regression using Scikit-Learn

I was trying to implement a model to distinguish between low or high pass filters acting on a white noise signal by using Scikit Learn's logistic regression. It seems to be working fine but when I ...
2
votes
1answer
250 views

Sklearn - Override random_state=None by default

Many scikit-learn and pandas objects/functions use random_state=None as a default parameter. How can it be overridden to ...
1
vote
2answers
171 views

Do models without parameters exist?

I am reading "A Course in Machine Learning" and, in chapter 2, the author says: "For most models, there will be associated parameters. These are the things that we use the data to decide on. ...
0
votes
1answer
91 views

Can the learning rate be considered both a parameter AND a hyper-parameter?

Here is my understanding of those 2 terms: Hyper-parameter: A variable that is set by a human before the training process starts. Examples are the number of hidden-layers in a Neural Network, the ...
-1
votes
1answer
316 views

Q: xgboost regressor training on a large number of indicator variables results in same prediction for all rows in test

I'm training a XGBoost regressor in Python on a data set with a large number of indicator variables (one-hot-encoded from categorical variables) and a few numerical variables.The dataset size is over ...
0
votes
1answer
2k views

How can I know how to interpret the output coefficients (`coefs_`) from the model sklearn.svm.LinearSVC()?

I'm following Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido, and in Chapter 2 a demonstration of applying ...
5
votes
3answers
2k views

What is the correct way to compute Mean F1 score?

I have a set of 10 experiments that compute precision, recall and f1-score for each experiment. Now, average precision & average recall is easy to compute. I have some confusion regarding average ...
1
vote
1answer
4k views

What is the difference of R-squared and adjusted R-squared?

I have in mind that R-squared is the explained variance of the response by the predictors. But i'd like to know how the adjusted value is computed ? and if the concept has any change from the original....
17
votes
6answers
11k views

What is the difference between model hyperparameters and model parameters?

I have noticed that such terms as model hyperparameter and model parameter have been used interchangeably on the web without prior clarification. I think this is incorrect and needs explanation. ...
-1
votes
1answer
73 views

Avoiding leakage on my random forest?

I am training a random forest model. I am wondering if it is safe (leakage?) to use on my training set the parameter average price of a car calculated using all my data points. The issue is that some ...
6
votes
3answers
232 views

Regression model with variable number of parameters in dataset?

I work in physics. We have lots of experimental runs, with each run yielding a result, y and some parameters that should predict the result, ...
0
votes
1answer
46 views

Missing features for classifier [closed]

If I am given 60 features along with test label and I was to find values of other features what is the best way to do it ?
2
votes
3answers
1k views

What are the best ways to tune multiple parameters?

When building a model in Machine Learning, it's more than common to have several "parameters" (I'm thinking of real parameter like the step of gradient descent, or things like features) to tune. We ...
4
votes
2answers
379 views

“Relearning” parameters

If this is a duplicate, I apologize. I'm not really sure what to even search for to try and find a duplicate/answer! We are working on a system for providing musical feedback to change the 'mood' of ...
4
votes
1answer
54 views

Parameter estimation: reduce time

I have a two-class prediction model; it has n configurable (numeric) parameters. The model can work pretty well if you tune those parameters properly, but the ...
2
votes
1answer
309 views

Finding parameters with extreme values (classification with scikit-learn)

I am currently working with the forest cover type prediction from Kaggle, using classification models with scikit-learn. My main purpose is learning about the different models, so I don't pretend to ...
1
vote
2answers
457 views

How can the performance of a neural network vary considerably without changing any parameters?

I am training a neural network with 1 sigmoid hidden layer and a linear output layer. The network simply approximates a cosine function. The weights are initiliazed according to Nguyen-Widrow ...
6
votes
1answer
1k views

Choosing a window size for DTW

I have time series data from mobile sensors for different motions such as walking, pushups, dumbellifts, rowing and so on. All these motions have different length of time series. For classifying them ...
46
votes
5answers
25k views

When is a Model Underfitted?

Logic often states that by underfitting a model, it's capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the ...
20
votes
2answers
14k views

What does the alpha and beta hyperparameters contribute to in Latent Dirichlet allocation?

LDA has two hyperparameters, tuning them changes the induced topics. What does the alpha and beta hyperparameters contribute to LDA? How does the topic change if one or the other hyperparameters ...