Questions tagged [parameter]
The parameter tag has no usage guidance.
45
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Bulding Deep Learning model for multiclassification case
I am soo confused i read a lot of information in forumas and still cna't get what is wrong.
my data is around 500.000 rows and 32 columns. my target variables consists of 3 classes (0, 1, 2). Hyperopt ...
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2
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How can you get a Huggingface fine-tuning model with the Trainer class from your own text where you can set the arguments for truncation and padding?
I want to find out the role of truncation and padding in Huggingface Transformers pretrained models and any fine-tuning model on top of that. Therefore I played around with these parameters, but I ...
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Should you care about truncation and padding in an LLM even if it has a very large tokenizer.max_length so that truncation will never happen?
I want to find out the role of truncation and padding in Huggingface Transformers pretrained models and/or any fine-tuning models on top. Taking a large language model like the German GPT2 shows that ...
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Why is 0.7, in general, the default value of temperature for LLMs?
I have recently read through a lot of documentation and articles about Large Language Models (LLMs), and I have come to the conclusion that 0.7 is, most of the time, the default value for the ...
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How to autosave a mode parameters during training neural network?
I am still new to python and NN. As NNs trainings is done for example 500 epochs, how I can auto-save the model so that if my connection gets lost or ran out of google GPU, next time I do not start ...
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1
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65
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eval_metric of XGBoost // ML model in general
Say I am using Xgboost on a binary classification task. eval_metric is one of the model parameter. How should I think about the impact of using different eval_metric(e.g rmse/mae/logloss) in general? ...
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kernel methods and parameter updates
Background information:
(it might be helpful to read the first 5 pages of this:https://cs229.stanford.edu/summer2020/cs229-notes3.pdf before answering the question).
I’m currently learning machine ...
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567
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what exactly is the "order"-parameter in pandas interpolation?
what exactly is the "order"-parameter in pandas interpolation?
it is mentioned in the doc:
...
2
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1
answer
200
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Number of parameters in CNN
I'm trying to understand the convolutional neural network and especially its parameters. I found several formulas on the internet, but I cannot understand them. For example:
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30
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Is this a task of meta-learning or transfer learning?
I have a task that I am not able to identify if it is of transfer or meta learning. I want to know this, in order to ask help in solving it, because there are some parts that I have not understood.
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Distplot 'a' parameter, does not make logical sense to me, can anyone explain?
Most sns plot parameters have names that make somewhat logical sense,
For example scatterplot has the x attribute to choose what to plot on the x-axis, and y for y-axis. Similarly kdeplot uses the ...
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78
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Can I apply different hyper-parameters for different sliding time windows?
Question
Can I apply different hyper-parameters for different training sets?
I can see the point of using the shared parameters but I cannot see the point of using shared hyper-parameters. The ...
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Can I say that a trained neural network model with less parameters requires less resources during real world inference?
Let us imagine that we have two trained neural network models with different architectures (e.g., type of layers). The first model (a) uses 1D convolutional layers with fully-connected layers and has ...
2
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How does C have effects on bias and variance of a Support Vector Machine?
The minimization problem for SVM can be written as-
$$\overset{\text{min}}{\theta} C\sum_{i = 1}^{m}{[y^icost_1(\theta^Tx^i) + (1-y^i)cost_0(\theta^Tx^i)]} + \frac12\sum_{j = 1}^n{\theta_j}^2$$
Now, ...
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Why does bidirectional LSTM have half the parameter count compared to LSTM in keras?
I want to implement a unidirectional and a bidirectional LSTM in tensorflow keras wrapper with the same amount of units. As an example I implement the unidirectional LSTM with 256 units, and the ...
2
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1
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189
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how to find the best parameters to solve a differential equation? [closed]
I have a differential equation:
def func(Y, t, r, p, K, alpha):
return r * (Y ** p) * (1 - (Y / K) ** alpha)
and I want to find the best parameters that fit (r,...
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How should rolling window of parameter estimates look like?
I am using Orstein-Uhlenbeck model to model inflation: $dI_t=\theta(\mu-I_t)dt+\sigma dW_t$.
I have plotted rolling window estimates of all the parameters. However, I do not understand what to ...
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78
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How does regularization help?
What is the effect of regularization on the value of parameters/weights?
How does adding a regularization term in the cost function(J) and gradients help?
Doesn't adding something increase the cost ...
2
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Four parameter self-starting function based on SSfpl
I am currently working with a self-starting function for four parameters which I based on SSfpl but with a different formula.
This is the formula for my self-starting function:
...
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1
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111
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How is DS used in the case of Payment Gateways?
I know it's a general question but what type of analytics can be done in this case?
How can we apply machine learning models here?
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773
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Face detection for different poses more robust than MTCNN?
I am using the MTCNN model described on machinelearningmastery here: MTCNN ipazc
But it won't detect certain orientations, ie. somebody lying on the ground so the top of the head points to the right ...
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1
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368
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Derivative of activation function used in gradient descent algorithms
Why is it necessary to calculate the derivative of activation functions while updating model( regression or NN) parameters? Why is the constant gradient of linear functions considered as a ...
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Max pooling has no parameters and therefore doesn't affect the backpropagation?
I feel this is a question that has a lot of variations already posted but it doesn't exactly answer my question. I understand the concept of max pooling and also the concept of backpropagation. What i ...
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68
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Generative network understanding
I was going through GAN's notebook by fchallot on Generative Adversarial Networks where, in the Generator Network, he creates a Dense layer with $16*16 * 128$ (...
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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) ...
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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 ...
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1
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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 ...
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2
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995
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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. ...
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1
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495
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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 ...
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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 ...
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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 ...
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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 ...
3
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1
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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....
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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. ...
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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 ...
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684
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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, ...
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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 ?
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3
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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 ...
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2
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"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 ...
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1
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68
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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 ...
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1
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469
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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 ...
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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 ...
7
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1
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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 ...
65
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6
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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 ...
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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 ...