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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10
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5answers
30k views

Clustering with cosine similarity

I have a large data set and a cosine similarity between them. I would like to cluster them using cosine similarity that puts similar objects together without needing to specify beforehand the number ...
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3answers
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Anomaly detection on time series

I've just started working on an anomaly detection development in Python. My data sets are a collection of timeseries. More in details, data are coming from some sensors/meters which record and ...
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2answers
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Why don't tree ensembles require one-hot-encoding?

I know that models such as random forest and boosted trees don't require one-hot encoding for predictor levels, but I don't really get why. If the tree is making a split in the feature space, then isn'...
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1answer
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XGBRegressor vs. xgboost.train huge speed difference?

If I train my model using the following code: ...
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5answers
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What is the difference between explainable and interpretable machine learning?

O’Rourke says that explainable ML uses a black box model and explains it afterwards, whereas interpretable ML uses models that are no black boxes. Christoph Molnar says interpretable ML refers to the ...
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8answers
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Definition of a model in machine learning

This definition does not quite apply since we are not always assuming an underlying distribution. So what is a model really? Can a Gradient Boosted Model (GBM) with specified hyperparameters be ...
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1answer
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Why neural networks do not perform well on structured data?

I was recently working on some classification problem where decision trees performed better than neural networks. I had tried various combinations with neural networks altering the number of neurons / ...
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1answer
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What is Hellinger Distance and when to use it?

I am interested in knowing what really happens in Hellinger Distance (in simple terms). Furthermore, I am also interested in knowing what are types of problems that we can use Hellinger Distance? ...
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4answers
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Prediction interval around LSTM time series forecast

Is there a method to calculate the prediction interval (probability distribution) around a time series forecast from an LSTM (or other recurrent) neural network? Say, for example, I am predicting 10 ...
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2answers
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How many images per class are sufficient for training a CNN

I'm starting a project where the task is to identify sneaker types from images. I'm currently reading into TensorFlow and Torch implementations. My question is: how many images per class are required ...
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3answers
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With unbalanced class, do I have to use under sampling on my validation/testing datasets?

I’m a beginner in machine learning and I’m facing a situation. I’m working on a Real Time Bidding problem, with the IPinYou dataset and I’m trying to do a click prediction. The thing is that, as you ...
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1answer
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What is difference between one hot encoding and leave one out encoding?

I am reading a presentation and it recommends not using leave one out encoding, but it is okay with one hot encoding. I thought they both were the same. Can anyone describe what the differences ...
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1answer
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How does the naive Bayes classifier handle missing data in training?

Naive Bayes apparently handles missing data differently, depending on whether they exist in training or testing/classification instances. When classifying instances, the attribute with the missing ...
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3answers
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Is TensorFlow a complete Machine Learning Library?

I am new to TensorFlow and I need to understand the capabilities and shortcomings of TensorFlow before I can use it. I know that it is a deep learning framework, but apart from that which other ...
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1answer
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How to split train/test in recommender systems

I am working with the MovieLens10M dataset, predicting user ratings. If I want to fairly evaluate my algorithm, how should I split my training v. test data? By default, I believe the data is split ...
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1answer
22k views

How to measure the similarity between two images?

I have two group images for cat and dog. And each group contain 2000 images for cat and dog respectively. My goal is try to cluster the images by using k-means. Assume image1 is ...
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1answer
8k views

Data preprocessing: Should we normalise images pixel-wise?

Let me present you with a toy example and a reasoning on image normalisation I had: Suppose we have a CNN architecture to classify NxN grayscale images in two categories. Pixel values range from 0 (...
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2answers
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"Deep Noether's Theorem": Building in Symmetry Constraints

If I have a learning problem that should have an inherent symmetry, is there a way to subject my learning problem to a symmetry constraint to enhance learning? For example, if I am doing image ...
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4answers
16k views

How does Sigmoid activation work in multi-class classification problems

I know that for a problem with multiple classes we usually use softmax, but can we also use sigmoid? I have tried to implement digit classification with sigmoid at the output layer, it works. What I ...
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3answers
5k views

Build a tool for manually classifying training data images

I have a large number of images that I need to classify for training a clustering algorithm, and I would like to do so offline (the data is proprietary). Basically, I'd like to build a desktop survey ...
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1answer
5k views

What's the difference between Error, Risk and Loss?

When we talk about 'Minimizing Loss', we often talk about loss functions such as Mean Squared Error (MSE); the term 'Empirical Risk Minimization' is often used interchangeably. So what's the ...
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7answers
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Is Python a viable language to do statistical analysis in?

I originally came from R, but Python seems to be the more common language these days. Ideally, I would do all my coding in Python as the syntax is easier and I've had more real life experience using ...
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1answer
10k views

What are the pros and cons of Keras and TFLearn?

What are the pros and cons of Keras and TFlearn? When is one library preferred over the other?
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3answers
33k views

Image resizing and padding for CNN

I want to train a CNN for image recognition. Images for training have not fixed size. I want the input size for the CNN to be 50x100 (height x width), for example. When I resize some small sized ...
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3answers
655 views

Why are ensembles so unreasonably effective

It seems to have become axiomatic that an ensemble of learners leads to the best possible model results - and it is becoming far rarer, for example, for single models to win competitions such as ...
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5answers
12k views

When to remove correlated variables

Can somebody please suggest what is the correct stage to remove correlated variables before feature engineering or after feature engineering ?
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4answers
5k views

Machine Learning vs Deep Learning

I am a bit confused by the difference between the terms "Machine Learning" and "Deep Learning". I have Googled it and read many articles, but it is still not very clear to me. A known definition of ...
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4answers
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Interpreting Decision Tree in context of feature importances

I'm trying to understand how to fully understand the decision process of a decision tree classification model built with sklearn. The 2 main aspect I'm looking at are a graphviz representation of the ...
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1answer
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HOW TO: Deep Neural Network weight initialization

Given difficult learning task (e.g high dimensionality, inherent data complexity) Deep Neural Networks become hard to train. To ease many of the problems one might: Normalize && handpick ...
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2answers
12k views

How to update bias and bias's weight using backpropagation algorithm

I'm writing my own training algorithm, but I don't know how to set the bias weight. Have I to set bias in any layer? Must the bias weight, be updated in every layer?
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3answers
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Sentiment Analysis Tutorial

I am trying to understand sentiment analysis and how to apply it using any language (R, Python etc). I would like to know if there is a good place on internet for tutorial that I can follow. I googled,...
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2answers
7k views

For which real world data sets does DBSCAN surpass K-means.?

For clustering, DBSCAN surpass k-means in terms of handling arbitrary shape data sets. In the most published papers about density based clustering, the experiments are performed with synthetic data ...
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1answer
6k views

Why we use information gain over accuracy as splitting criterion in decision tree?

In decision tree classifier most of the algorithms use Information gain as spiting criterion. We select the feature with maximum information gain to split on. I think that using accuracy instead of ...
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1answer
3k views

Regularization in simple math explained

I read a lot of articles online about how regularization works and most of them just show the equations with regularization terms but did not use example numbers to explain how the coefficient values ...
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2answers
35k views

How to calculate the fold number (k-fold) in cross validation?

I am confused about how I choose the number of folds (in k-fold CV) when I apply cross validation to check the model. Is it dependent on data size or other parameters?
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4answers
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What does the output of model.predict function from Keras mean?

I have built a LSTM model to predict duplicate questions on the Quora official dataset. The test labels are 0 or 1. 1 indicates the question pair is duplicate. After building the model using ...
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2answers
3k views

Visualizing deep neural network training

I'm trying to find an equivalent of Hinton Diagrams for multilayer networks to plot the weights during training. The trained network is somewhat similar to a Deep SRN, i.e. it has a high number of ...
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2answers
30k views

Validation loss and accuracy remain constant

I am trying to implement this paper on a set of medical images. I am doing it in Keras. The network essentially consists of 4 conv and max-pool layers followed by a fully connected layer and soft max ...
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2answers
13k views

How do "intent recognisers" work?

Amazon's Alexa, Nuance's Mix and Facebook's Wit.ai all use a similar system to specify how to convert a text command into an intent - i.e. something a computer would understand. I'm not sure what the "...
11
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4answers
27k views

How to avoid overfitting in random forest?

I want to avoid overfitting in random forest. In this regard, I intend to use mtry, nodesize, and maxnodes etc. Could you please help me choose values for these parameters? I am using R. Also, if ...
9
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1answer
7k views

Are the raw probabilities obtained from XGBoost, representative of the true underlying probabilties?

1) Is it feasible to use the raw probabilities obtained from XGBoost, e.g. probabilities obtained within the range of 0.4-0.5, as a true representation of approximately 40%-50% chance of an event ...
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5answers
2k views

Time-series grouped cross-validation

I have data with the following structure: ...
4
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3answers
610 views

Why models performs better If normalize test data and train data separately?

Many threads (and courses) such as this and this one suggest that you should apply normalization to the test data using the parameters used in the training set. But other some discussions I've found ...
15
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4answers
20k views

How to maximize recall?

I'm a little bit new to machine learning. I am using a neural network to classify images. There are two possible classes. I am using a Sigmoid activation at the ...
15
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3answers
4k views

Predict the best time of call

I have a dataset including a set of customers in different cities of California, time of calling for each customer, and the status of call (True if customer answers the call and False if customer does ...
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4answers
4k views

How will Occam's Razor principle work in Machine learning

The following question displayed in the image was asked during one of the exams recently. I am not sure if I have correctly understood the Occam's Razor principle or not. According to the ...
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3answers
11k views

What are the consequences of not freezing layers in transfer learning?

I am trying to fine tune some code from a Kaggle kernel. The model uses pretrained VGG16 weights (via 'imagenet') for transfer learning. However, I notice there is no layer freezing of layers as is ...
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1answer
1k views

How to determine the complexity of an English sentence?

I am working on an app to help people learn English as a second language. I have validated that sentences help in learning a language by providing extra context. I did that by conducting a small ...
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2answers
7k views

Proper way of fighting negative outputs of a regression algorithms where output must be positive all the way

Maybe it is a bit general question. I am trying to solve various regression tasks and I try various algorithms for them. For example, multivariate linear regression or an SVR. I know that the output ...
8
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3answers
6k views

Text classification with thousands of output classes in Keras

Task: I have a dataset with job titles and descriptions. The task is to predict tags for job by job title and description. There are several tags for each job posting. Therefore, the number of ...

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