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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14
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1answer
15k views

How does Gradient Descent and Backpropagation work together?

Please forgive me as I am new to this. I have attached a diagram trying to model my understanding of neural network and Back-propagation? From videos on Coursera and resources online I formed the ...
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3answers
14k views

Cross validation Vs. Train Validate Test

I have a doubt regarding the cross validation approach and train-validation-test approach. I was told that I can split a dataset into 3 parts: Train: we train the model. Validation: we validate and ...
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3answers
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How to choose a classifier after cross-validation?

When we do k-fold cross validation, should we just use the classifier that has the highest test accuracy? What is generally the best approach in getting a classifier from cross validation?
14
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1answer
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Back-propagation through max pooling layers

I have a small sub-question to this question. I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was ...
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2answers
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Is there away to change the metric used by the Early Stopping callback in Keras?

When using the early stopping callback in Keras, training stops when some metric (usually validation loss) is not increasing. Is there a way to use another metric (like precision, recall, or f-measure)...
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2answers
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Why do activation functions have to be monotonic?

I am currently preparing for an exam on neural networks. In several protocols from former exams I read that the activation functions of neurons (in multilayer perceptrons) have to be monotonic. I ...
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4answers
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What are the implications for training a Tree Ensemble with highly biased datasets?

I have a highly biased binary dataset - I have 1000x more examples of the negative class than the positive class. I would like to train a Tree Ensemble (like Extra Random Trees or a Random Forest) on ...
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4answers
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How to make a decision tree with both continuous and categorical variables in the dataset?

Let's say I have 3 categorical and 2 continuous attributes in a dataset. How do I build a decision tree using these 5 variables? Edit: For categorical variables, it is easy to say that we will split ...
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6answers
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Python: Handling imbalance Classes in python Machine Learning

I have a dataset for which I am trying to predict target variables. ...
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1answer
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Multiple Categorical values for a single feature how to convert them to binary using python

I have a data set of movies which has 28 columns. One of them is genres. For each row in this data set, the value for column genres is of the form "Action|Animation|Comedy|Family|Fantasy". I want to ...
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3answers
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What is a channel in a CNN?

I was reading an article about convolutional neural networks, and I found something that I don't understand, which is: The filter must have the same number of channels as the input image so that the ...
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5answers
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Why does adding a dropout layer improve deep/machine learning performance, given that dropout suppresses some neurons from the model?

If removing some neurons results in a better performing model, why not use a simpler neural network with fewer layers and fewer neurons in the first place? Why build a bigger, more complicated model ...
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2answers
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When do we say that the dataset is not classifiable?

I have many times analysed a dataset on which I could not really do any sort of classification. To see whether I can get a classifier I have usually used the following steps: Generate box plots of ...
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2answers
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Activation function between LSTM layers

I'm aware the LSTM cell uses both sigmoid and tanh activation functions internally, however when creating a stacked LSTM architecture does it make sense to pass their outputs through an activation ...
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1answer
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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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3answers
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Is feature selection necessary?

I would like to run some machine learning model like random forest, gradient boosting, or SVM on my dataset. There are more than 200 predictor variables in my dataset and my target classes are a ...
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2answers
1k views

Machine Learning - Where is the difference between one-class, binary-class and multinominal-class classification?

Where is the difference between one-class, binary-class and multinominal-class classification? If I like to classify text in lets say four classes and also want the system to be able to tell me that ...
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6answers
1k views

Is there any way to explicitly measure the complexity of a Machine Learning Model in Python

I'm interested in model debugging and one of the points that it mentions is to compare your model with a "less complex" one in order to check if the performance is substantially better on ...
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3answers
6k views

Help regarding NER in NLTK

I have been working in NLTK for a while using Python. The problem I am facing is that their is no help available on training NER in NLTK with my custom data. They have used MaxEnt and trained it on ...
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3answers
10k views

Incremental Learning with sklearn: warm_start, partial_fit(), fit()

I have built an ML model with the goal of making predictions for targets of the following week. In general, new data will come in and be processed at the end of each week and be in the same data ...
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3answers
5k views

Is there any proven disadvantage of transfer learning for CNNs?

Suppose I know that I want to use a ResNet-101 architecture for my specific problem. There are ReseNet-101 models trained on ImageNet. Is there any disadvantage of using those pre-trained models and ...
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3answers
5k views

Twitter Sentiment Analysis: Detecting neutral tweets despite training on only Positive and Negative Classes

I am a newbie when it comes to machine learning. I am trying to get hands on experience by analyzing different supervised learning algorithms using scikit-learn library of python. I am using the ...
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2answers
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What is the purpose of multiple neurons in a hidden layer?

On the surface, this sounds like a pretty stupid question. However, i've spent the day poking around various sources and can't find an answer. Let me make the question more clear. Take this ...
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4answers
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Why positive-unlabeled learning?

Machine learning can be divided into several areas: supervised learning, unsupervised learning, semi-supervised learning, learning to rank, recommendation systems, etc, etc. One such area is PU ...
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1answer
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Why does logistic regression in Spark and R return different models for the same data?

I've compared the logistic regression models on R (glm) and on Spark (LogisticRegressionWithLBFGS) on a dataset of 390 obs. of ...
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1answer
4k views

Anomaly detection for transaction data

I have transaction details for credit data (bank transfers, peer to peer transfers, etc). Currently, I have one year worth of data which I cannot properly classify. I'm looking for input and ...
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2answers
19k views

Get multiple output from Keras

I have a regression problem which I have to predict 3 numerical values from a provided data. For example let's say I have a data set containing ...
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2answers
6k views

How to further improve the kaggle titanic submission accuracy?

I am working on the Titanic dataset. So far my submission has 0.78 score using soft majority voting with logistic regression and random forest. As for the features, I used Pclass, Age, SibSp, Parch, ...
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3answers
2k views

Categorizing Customer Emails

I am working on a project for a company which needs to categorize customer e-mails regarding loans and insurance. The e-mails are labeled uniquely from set of 13 category labels. The number of records ...
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1answer
323 views

ANN on Pattern Recognition

I have been trying to apply a simple neural network using keras to predict a sequence of numbers and the rule is if the input integer is odd it should be 4 and if its even it should be 2. Yet the ...
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3answers
19k views

Can GPS coordinates (latitude and longitude) be used as features in a linear model?

I have data sets that contain, among many features, GPS coordinates (latitude and longitude). I'd like to use these data sets to explore problems such as: (1) computing ETA to drive between start and ...
8
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2answers
954 views

Why is taking the gradient of the average error in SGD not correct, but rather the average of the gradients of single errors?

I am a little confused about taking averages in cost functions and SGD. So far I always thought in SGD you would compute the average error for a batch and then backpropagate it. But then I was told in ...
6
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2answers
25k views

Keras - no prediction probability for multiple output models?

I have built the following model: ...
4
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1answer
491 views

Can a novelty detection model overfit?

Can a novelty detection model overfit? In novelty detection, the model is trained on normal data instances (not polluted by outliers) where no labels are used in the training process, while validated ...
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2answers
2k views

What are the default values of nodes and internal layers in Neural Network models?

What is the default number of internal layers and internal nodes in training a neural network? My data has 62 observations with roughly 200 predictors. I have a target variable with two classes and ...
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2answers
1k views

Cluster documents based on topic similarity

I have set of documents where I have assigned topics per each document. E.g., Topics of document 1 -> 1.0 Science, 1.0 politics, 0.8 History, 0. 8 Information and Technology Now I want to cluster ...
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1answer
613 views

Data Snooping, Information Leakage When Performing Feature Normalization

Assume that we have a training data set (with both features and labels) and a test data set (with only features). When we build a machine learning model that requires normalization of the features, ...
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1answer
173 views

Interpreting vertical and horizontal parts of ROC curve

It's not clear to me how I can interpret vertical and horizontal parts of the ROC curve. What important information can I gain from this? This is a text from the book "Human-in-the-Loop Machine ...
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3answers
12k views

Why do we use a Gaussian kernel as a similarity metric?

In graph-based clustering, why is it preferred to use the Gaussian kernel rather than the distance between two points as the similarity metric?
8
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1answer
885 views

Question on bias-variance tradeoff and means of optimization

So I was wondering how does one, for example, can best optimize the model they are trying to build when confronted with issues presented by high bias or high variance. Now, of course, you can play ...
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1answer
3k views

Using time series data from a sensor for ML

I have the following data for a little side project. It's from an accelerometer sitting on top of a washer/dryer and I'd like it to tell me when the machine has finished. x is the input data (x/y/z ...
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2answers
6k views

Recommender system based on purchase history, not ratings

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account i) product holdings ii) user characteristics (segment, age, affluence, etc.). I ...
6
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2answers
644 views

Why an increasing validation loss and validation accuracy signifies overfitting?

When I train a neural network, I observe an increasing validation loss, while at the same time, the validation accuracy is also increased. I have read explanations related to the phenomenon, and it ...
6
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2answers
1k views

Classifying survey response text SVM

I have 800 responses to an open-ended survey question. Each response is categorized into 3 categories based on a list of 70 categories. These categories are things like "stronger leadership", "better ...
4
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1answer
178 views

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 ...
4
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1answer
203 views

Fine-tuning a CNN for recognizing two classes, but also being able to tell if none of them is present in an image

I need to fine-tune a CNN to classify two classes: dogs and cats, for example. However, I want the CNN to be able to tell if ...
4
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1answer
3k views

Machine Learning Identification and Classification, based on string contents: General advice

I have just very recently started to develop an interest in machine learning, and I have a particular problem in mind that I would like to start to explore. I would like to train a system to ...
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2answers
3k views

Should the bias value be added after convolution operation in CNNs?

Should we add bias to each entry of the convolution then sum, or add bias once at end of calculating the convolution in CNNs?
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1answer
3k views

Example of 1D ConvNet filter

I understand Conv2D filters. I think I understand Conv1D filters as well but have not seen any examples of the filters like what ...
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2answers
4k views

How to reduce the error in linear regression [closed]

In linear regression, how can we minimize the error term?

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