Questions tagged [machine-learning-model]
A machine learning model is a simplified representation of a dataset, derived from statistics in the data, used to make predictions. It can represent patterns, behaviours or features within this dataset which have been learnt by the algorithm during training.
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Incremental learning on Autoencoder for anomaly detection
I want to incrementally train my pre-trained autoencoder model on data being received every minute. Based on this thread, successive calls to model.fit will incrementally train the model. However, the ...
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Which metric to use to evaluate prediction problem
The product manager wants to know if you can develop a model to predict the number of
views a listing will receive based on the boat's features. She would consider using your model if, on average, the ...
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how to improve recall by retraining a model on its feedback
I am creating a supervised model using sensitive and scarce data. For the sake of discussion, I've simiplified the problem statement by assuming that I'm creating a model for identifying dogs.
Let's ...
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Is there any works in the direction of dimensionally reducing the size of DNNs?
I am talking about a scenario where you first train a "huge" Neural Network and then try to scale it down without sacrificing much of the accuracy. I am not talking about quantization of ...
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Is it possible for a feature not correlated with a dependent variable to become important in a machine learning model?
Is it possible for a feature not correlated (or faintly correlated) with a dependent variable to become important in a machine learning model?
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Text classification with Weka (unlimited dependent variable values)
In our dataset we have 2 attributes, citizen and nric. The rule is if citizen is US, then the result should be the nric value, otherwise Non-US.
Could you please suggest which algorithm in Weka I ...
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How to compare new model to current production model?
Given new data, I trained the same model architecture and same hyperparameters (for example a random forest) as the current production model. How do I know that the new model that I trained is better ...
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Time series test data dilema
I’m trying to build a model to predict the amount of sales of a product for the next few days
This question is about whether or not I should use the tail of the serie as the test set and train models ...
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Evaluate two Recommender models trained with different data
Suppose you are given two Recommender Systems to evaluate, A and B. Model A is trained with ...
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Classification for two dimensional data
I have time series like 500 data points of $(x,y)$ pairs, where $x$ = time in seconds and $y$ = signals. Each of these candidates/time series has an additional label, which tells about the nature of ...
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Trying to perform elastic-net regression in R
I am new to R and Elastic-Net Regression Model. I am running Elastic-Net Regression Model on the default dataset, titanic. I am trying to obtain the Alpha and Lambda values after running the train ...
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Uncertainty about shape of ROC curve
I am working on a binary classification and the plotted ROC curves that I am using for evaluation together with AUC, have seemed strange to me. Here is an example.
I understand that ROC is a visual ...
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Are most deep learning models online learning models?
I'm online learning starter. from my perspective, online learning model is the model which can update its paramater with data flows(I've seen a article pointing out that incremental model is ...
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Recommendation for sklearn model persistent
I want to serve a sklearn model in server, any suggestions what is best format/method to save sklearn models. Currently I am ...
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Recommendations for tuning XGBoost Hyperparams?
XGBoost has quite a few hyperparameters to tune: max depth, min child weight, number of iterations, eta, gamma, percent of columns considered, and percent of samples considered.
It's computationally ...
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Should we apply transformation to test and new data?
I am creating a model to predict customer churn. Checking this post, a couple of doubts came up:
-All the transformation we do when training the data, it is necessary to perform also when predicting? ...
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Can I include a quotient as dependent variable and independent variables with same denominator in a linear model? How do we interpret such models?
I want to create a model in a food processing plant where my dependent variable is Electricity (KWhr) consumption per kg. Plant produce different food items with varying electricity consumption. I'm ...
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Build autoencoder for single matrix with integer numbers
Can you please tell me how to build an autoencoder with a single matrix(4,4) with integer numbers?
I want to build an autoencoder for the below-mentioned data. I don't know whether I should convert ...
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Why best hyperparameters leads to drop in test performance?
I am working on a binary classification problem using random forests (75:25 - class proportion). Label 0 is minority class. So, I am following the below approach
a) execute RF with default ...
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Why is my test accuracy higher than train accuracy? SKLEARN
As the title say my code produces low train accuracy and high test accuracy.
First I split my data set to train and test sets.
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Encoding before vs after train test split?
Am new to ML and working on a dataset with lot of categorical variables with high cardinality.
I observed that in lot of tutorials for encoding like here, the encoding is applied after the train and ...
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Auto-ML for only fixed estimator
Am working on a binary classification with 1000 rows and 28 columns.
I would wish to use an Auto-ML solution to try out different combinations of hyperparameters etc but the algo should only be ...
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Is there an indicator to know if the predicted value is 100% right?
I am new to Machine Learning.
I want to know if there is any indicator which can show us ML's confidence about any given prediction.
I am suppose to build an application in which I only want to use ...
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Converting binary valued data to boolean or one hot?
I am dealing with a dataset that contains multiple columns (features) that contain binary variables, e.g., a gender feature that contains 'male' and 'female'.
I want to apply some supervised learning ...
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String Matching Classification and Scoring
I've been playing with a problem around matching product names. I've trained a model based on a variety of different features (numbers only, Levenshtein distance, pulling out package sizes, brands, ...
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Is the RNN vanishing gradients problem really a gradients problem?
It is known that RNNs do not have long memory, that is they do not capture long dependencies.
Usually this is explained by the vanishing (or explolding) gradients problem - when computing the ...
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name 'layers' is not defined [closed]
I am trying to use EfficientNetB7 from keras implementation
Image classification via fine-tuning with EfficientNet
but always the following code gives me error:
...
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Transform multi-class problem to multi-label problem
I found this question but I need an answer to the other direction.
Example:
Let's say we want to predict if a person with a certain profile wants to buy product A and/or B. So we have 2 binary classes ...
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Sentence Classification Machine Learning API
Are there any ML models or APIs that can be used to classify a sentence into one of the four types of sentences; i.e. declarative (statement), imperative (command), interrogative (question) and ...
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Approaches for multiclass classification with a reference level to extract variables of importance?
I have a dataset with with multiple classes (< 20) which I want to classify in reference to one of the classes.The final goal is to extract the variables of importance which are useful to ...
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Why a CNN with decreasing filter layers sizes could perform better than a "regular one" with increasing sizes?
I did dozens (or probably hundreds) of tests and the best result with less total parameters(4 times or less) was a decreasing filter layers size architecture. This is a CNN for multiclass image ...
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Estimating the uncertainty of regression models
Given a regression model, with n features, how can I measure the uncertainty or confidence of the model for each prediction?
Suppose for a specific prediction the accuracy is amazing, but for another ...
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How could I estimate slope of lines on a scatter plot?
I have a list of coordinate pairs. To the human eye, they form lines with a constant slope:
This is how I generated that image above:
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Why is newer GPU slower than older one during training?
I am a very beginner in deep learning and am training a Tacotron 2 model written in PyTorch (see the link if it matters) on Google Colab and was lately granted an ...
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Test / Train split - is it always necessary (supervised learning)?
I am currently working on my first machine learning model (Palmer Penguins dataset). I am going to train the 3 machine learning models, each of them using the different model architecture (Decision ...
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gridsearchcv best coefficients do not match well with the perfect line
I wrote a program to find the best combination of coefficients to describe a variable. However, the coefficients from the gridsearchcv do not match well with the expected line. This is a sample of my ...
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What model to train to restore MNIST test dataset
I came across this problem, and not sure where to start. What model would work best for this problem and why?
Imagine the digits in the test set of the MNIST dataset
(http://yann.lecun.com/exdb/mnist/)...
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ValueError: Data cardinality is ambiguous: (Jupyter Notebook)
I'm building an OCR to read text off of water meters. I'm running into the error mentioned above when I try to fit the machine learning model. I am using the segmentation_models python library.
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What is The difference of xgboost.sklearn.XGBClassifier and xgboost.XGBClassifier?
xgboost.sklearn VS xgboost.XGBClassifier
Here is my code that I tried to train make_moons ...
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Gradient descent implementation of logistic regression
Objective
Seeking for help, advise why the gradient descent implementation does not work below.
Background
Working on the task below to implement the logistic regression.
Gradient descent
Derived the ...
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In multi-class, is the average accuracy of each class in confusion metrics equal to the accuracy calculated from cross validation?
When I calculate accuracy through cross-validation, it gives me a different accuracy than when I calculate through confusion metrics. Why does it give different accuracy? Is overall calculated ...
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Estimating sales for new products in e-commerce
I am trying to find an ML solution for the following problem.
Objective: Given a list of products of Stores B, C, D … estimate the Order Value (1 year timeframe, let’s say) that each product would ...
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How can I measure time and memory complexity for a deep learning model?
How can I measure or find the time complexity and memory complexity for a model like VGG16 or Resnet50? Also, will it be different from one machine to another like using GTX GPU or RTX GPU?
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How to learn common sense constants? Look in body for detail
If I wanted to learn constants for example week -> 7 days, chicken -> 2 legs, day -> 24, 1km -> 1000 meters hours, and so on, would it be possible to extract this information from a BERT ...
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How to go about predicting administrative fees?
We collect administrative fees from our customers based on many complex business rules albeit based on few variables. I have the history of fees colected through time (about 500 records for each ...
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How do I know if my model's result is good enough?
I have a dataset of different people with their insurance cost. I have trained a neural-network to predict the insurance cost (charges column) based on the other features (age,bmi, etc.). Here is how ...
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How can I choose the best machine learning algorithms from all kinds of algorithms?
When I want to find a model for my data set, I find that there are lots of algorithms that I can use. I know how to minimize selection choices by separating supervised and unsupervised algorithms and ...
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how to choose the best machine learning algorithms from all kinds of algorithms? [duplicate]
guys, I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher and after I’ve got to work with some real projects on ...
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One-hot-encoded variables dominating clustering
I am performing some unsupervised clustering with k-means on some transaction data that contains the following information:
Customer
units purchased in category_1
units purchased in category_1
time ...
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Why not LinearClassifer when RidgeClassifier works?
Recently I came across this model called RidgeClassifier,
It coverts the predicted value (y) to {-1,1} and then uses the Ridge Regression. During prediction if the value of y is < 0 then it is ...
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