Questions tagged [machine-learning]

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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Input for LSTM for financial time series directional prediction

I'm working on using an LSTM to predict the direction of the market for the next day. My question concerns the input for the LSTM. My data is a financial time series $x_1 \ldots x_t$ where each $x_i$...
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0answers
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AdaBoost implementation and tuning for high dimensional feature space in R

I am trying to implement the AdaBoost.M1 algorithm (trees as base-learners) to a data set with a large feature space (~ 20.000 features) and ~ 100 samples in R. ...
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0answers
84 views

Training value neural network AlphaGo style

I have been trying to replicate the results obtained by AlphaGo following their supervise learning protocol. The papers specify that they use a network that has two heads: a value head that predicts ...
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1answer
245 views

Training stateful LSTM with different number of sequences

I'm using a stateful LSTM for stock market analysis, and I have varying amounts of data for each stock, ranging from 20 years to just a few weeks (i.e. for newly listed stocks). I use 3 years of data ...
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1answer
137 views

How to model segmentation of a sequence to similar parts?

I guess LSTM is good for sequence modeling but how would you model "clustering" with it? Meaning, the input is a sequence and the output is labels with similar properties (I have labeled data). For ...
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2answers
85 views

Imbalanced training set vs smaller balanced training set?

Say I am using a maximum likelihood approach and my output unit computes a softmax function. My training set is distributed as follows over 6 classes: ...
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0answers
360 views

How to detect vanishing and exploding gradients with Tensorboard?

I have two "sub-questions" 1) How can I detect vanishing or exploding gradients with Tensorboard, given the fact that currently write_grads=True is deprecated in the Tensorboard callback as per "un-...
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2answers
170 views

Evaluation of regression models with different evaluations (MSE, variance, VAF etc.)

When comparing several regression models in terms of quality, it seems like most have agreed on the MSE. There are also papers comparing "variance" and "variance accounted for (VAF)&...
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How to predict advantage value in deep reinforcement learning

I'm currently working on a collection of reinforcement algorithms: https://github.com/lhk/rl_gym For deep q-learning, you need to calculate the q-values that should be predicted by your network. There ...
5
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1answer
114 views

Predicting change of shapes/coordinates

I'm trying to find a way to predict/calculate how a shape (e.g. outline of a glacier) will change in the future—based on its history (previous shape) and additional factors (e.g. Δtemperature). In my ...
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3answers
761 views

Hyperparameter tuning in multiclass classification problem: which scoring metric?

I'm working with an imbalanced multi-class dataset. I try to tune the parameters of a DecisionTreeClassifier, ...
5
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1answer
679 views

Time Series pattern recognition and classification problem

I have some labeled sensor data. Now, I would like to know how to extract features from time series using DFT, DWT, and HAAR transforms. I know that the transformations above transform a signal to ...
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495 views

differences between LSQR and FTRL when working with very sparse data

I have a 2M instances dataset with millions of very very sparse dummy variables created using the hashing trick = ...
5
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3answers
329 views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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1answer
152 views

Linear Regression bad results after log transformation

I have a dataset that has the following columns: The variable I'm trying to predict is "rent". My dataset looks a lot similar to what happens in this notebook. I tried to normalize the rent ...
4
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1answer
410 views

Self-attention mechanism did not improve the LSTM classification model

I am doing an 8-class classification using time series data. It appears that the implementation of the self-attention mechanism has no effect on the model so I think my implementations have some ...
4
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2answers
115 views

What to do when seed has a big impact on model performance?

I have a training procedure set up for an image recognition task. Each time I train a model, I record training loss, validation loss, validation precision and validation recall. Recently I switched ...
4
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1answer
67 views

What ML architecture fits fixed length signal regression?

My problem is of regression type - How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
4
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1answer
36 views

Output value of a gradient boosting decision tree node that has just a single example in it

The general gradient boosting algorithm for tree-based classifiers is as follows: Input: training set $\{(x_{i},y_{i})\}_{i=1}^{n}$, a differentiable loss function $L(y,F(x))$, and a number of ...
4
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1answer
165 views

Learning parameters when loss is a piecewise function

I have a network to generate a single number $T$. I know in advance: a property of the loss function is that, when $T \in [a_1, a_2]$, the loss has the same value $L_1$; when $T \in [a_2, a_3]$, the ...
4
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0answers
362 views

How to use multiple text features for NLP classifier?

I am trying to build text classifier, Usually, we have one text column and ground truth. But I am working on a problem where dataset contains many text features. I am exploring different ways how to ...
4
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1answer
145 views

How to incorporate new features in an existing machine learning model?

Suppose we have trained a regression model $M$ on a fixed set of $n$ features, $F_1,F_2,…,F_n$ on a particular dataset $G$. Now assume that after model training, additional features ($F_{n+1},…$) ...
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2answers
43 views

Backpropagation with a different sized training set?

I'm trying to create a NN whose input is a (length m) array of 3d vectors $$\vec{x}_i = [x_{i,1},x_{i,2},x_{i,3}], \hspace{5mm}i=1:m $$ and whose output is a similarly sized array: $$\vec{h}_{\theta,...
4
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1answer
151 views

tensorflow pseudo inverse doesn't work for complex matrices!

The Tensorflow documentation here says that: tf.linalg.pinv is ''analogous to numpy.linalg.pinv. It differs only in default value of rcond''. However, tf.linalg.pinv requires the matrix to ...
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0answers
26 views

How can I test the relationship between events and categorical data

I have a dataset comprised of mostly categorical data, in particular, different name tags of events that happen within a process. Those are accompanied of their <...
4
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1answer
70 views

Training Machine Learning Model - Neural Network - Islands Problem

I was working on the following leetcode problem: Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting ...
4
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4answers
236 views

Backpropagation: Relevance of the error signal of a neuron

During my quest to understand back propagation in a more rigorous approach I have come across with the definition of error signal of a neuron which is defined as follows for the $j^{\text{th}}$ neuron ...
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0answers
142 views

What does big O mean in KNN optimal weights?

Wiki gives this definition of KNN In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists ...
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2answers
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Saving and loading keras.callbacks.History object with np.save and np.load

I have been saving my training history in keras as follows: ...
4
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2answers
1k views

How can I use two different datasets as a training model for svm

I know that you're supposed to scale your test data using the parameters (mean and stdev) from your training data. This is relatively simple; but what if the number of samples is limited in one ...
4
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2answers
92 views

What are the reasons for drawing initial neural network weights from the Gaussian distribution?

Are there theoretical or empirical reasons for drawing initial weights of a multilayer perceptron from a Gaussian rather than from, say, a Cauchy distribution?
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1answer
84 views

EM-ELM Cross validation

I know that cross validation is used to find the best hyperparameters that minimize the average error. For example, the number of neurons that minimize the average error of cross-validation is ...
4
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0answers
321 views

Multiple activation functions with TensorFlow estimator DNNClassifier

I just want to know if is it possible to use tf.estimator.DNNClassifier with multiple different activation functions. I mean, could I use a DNNClassifier estimator which use different activation ...
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0answers
216 views

Confidence value in AdaBoost?

I read this introduction about AdaBoost (http://www.cs.man.ac.uk/~nikolaon/~nikolaon_files/Introduction_to_AdaBoost.pdf), and am curious why confidence for each model is defined as $$\alpha_j=\frac{...
4
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0answers
75 views

Classify driver based on time-series sensor data

I want to build a model that can detect which driver is driving now the car based on a dataset that contains 20 driver records for 3600s each driver ( the dataset contains all the car sensors values ...
4
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2answers
226 views

Support Vector Regression trained with data sets

I am now searching for a long time on the internet and on papers for an answers of simple questions. Am I able to train a Support Vector Regression algorithm with different data sets? If yes, how is ...
4
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2answers
3k views

fix first two levels of decision tree?

I am trying to build a regression tree with 70 attributes where the business team wants to fix the first two levels namely country and product type.To achieve this,I have two proposals: 1.Build a ...
4
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0answers
270 views

how to propagate error from convolutional layer to previous layer?

I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week. To be specific, assume there are 3 layers in a convolutional pass, marked as ...
4
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1answer
449 views

Illustrating the dimensionality reduction done by a classification or regression model

Tl;DR: You can predict something, but how do you explain the prediction? Your usual classification/regression setup Lets say the data is a classic regression/classification problem: several numerical ...
4
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1answer
217 views

How to automatically verify official documents?

I am new to machine learning and data science. I apologise if the question seems very basic. I have a requirement where I need to verify information submitted via a form with the corresponding ...
3
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0answers
32 views

Is it Scientifically Rigorous to create a multimodal ML Model with data from multiple sources

I'm attempting to create a multimodal machine learning model for disease diagnosis. However, I'm having quite a bit of difficulty finding public data sets with all the data I need. For example, I need ...
3
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3answers
102 views

Classification when the classification of the previous itens matter

I have a classification problem to solve, that seems to be common but I am struggling to find the name of this task and the best way to model this problem. Suppose I have a series of events that are ...
3
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0answers
26 views

Understanding an MLP coefficient array

I have implemented a super simple MLP using SKLearn. I have a 2 hidden layer model and 31 features on the input layer. So the lengths of the arays are 31, 20 and 10. ...
3
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0answers
74 views

Forecast Model to Estimate Customer Service Call Volume and Appropriate Staff

I am working on a project to predict the proper staffing needed for a customer service team using historical data. I am new to machine learning, and I am not sure if my approach to this problem is the ...
3
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0answers
62 views

Struggling to understand/implement Transformer Decoder

I'm struggling to understand the decoder in a Transformer model, specifically with regards to some aspects of its architecture as well as how it actually handles the data during training. What I have ...
3
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1answer
39 views

Keras use of ImageNet?

Keras mentions that it provided models pretrained on ImageNet. However, it doesn't specify what they mean by "ImageNet" - like is it a certain subset of ImageNet of the complete set of ...
3
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1answer
54 views

How do I know that model performance improvement is significant?

Say I am running a Machine Learning model that produces a certain result (say accuracy of 80%). I now change a minor detail in my model (say, in a Deep Learning model, increase the kernel size in one ...
3
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2answers
55 views

How to identify if there is a relationship between 5 categorical independent variables to a binary dependent variable?

My dataset has 5 independent variables, each with a value of either Large, Medium or None and a binary dependent variable. The dataset has 67 rows with a split of 17:50. I would like to identify if ...
3
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0answers
56 views

Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...
3
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0answers
74 views

Unbiasedness of random forests

Suppose that I am trying to build a random forest by subsampling the data and choosing a single feature per tree randomly. For example, suppose there is some dataset, $D = \{(x_{1},y_{1}), ......(x_{N}...

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