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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37
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6answers
8k views

How to set the number of neurons and layers in neural networks

I am a beginner to neural networks and have had trouble grasping two concepts: How does one decide the number of middle layers a given neural network have? 1 vs. 10 or whatever. How does one decide ...
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5answers
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Does gradient descent always converge to an optimum?

I am wondering whether there is any scenario in which gradient descent does not converge to a minimum. I am aware that gradient descent is not always guaranteed to converge to a global optimum. I am ...
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6answers
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Why do convolutional neural networks work?

I have often heard people saying that why convolutional neural networks are still poorly understood. Is it known why convolutional neural networks always end up learning increasingly sophisticated ...
42
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4answers
18k views

Is it always better to use the whole dataset to train the final model?

A common technique after training, validating and testing the Machine Learning model of preference is to use the complete dataset, including the testing subset, to train a final model to deploy it on, ...
172
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5answers
115k views

What is the "dying ReLU" problem in neural networks?

Referring to the Stanford course notes on Convolutional Neural Networks for Visual Recognition, a paragraph says: "Unfortunately, ReLU units can be fragile during training and can "die". For ...
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7answers
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In supervised learning, why is it bad to have correlated features?

I read somewhere that if we have features that are too correlated, we have to remove one, as this may worsen the model. It is clear that correlated features means that they bring the same information, ...
73
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7answers
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Open source Anomaly Detection in Python

Problem Background: I am working on a project that involves log files similar to those found in the IT monitoring space (to my best understanding of IT space). These log files are time-series data, ...
64
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5answers
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Adding Features To Time Series Model LSTM

have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to ...
59
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4answers
65k views

Why mini batch size is better than one single "batch" with all training data?

I often read that in case of Deep Learning models the usual practice is to apply mini batches (generally a small one, 32/64) over several training epochs. I cannot really fathom the reason behind this....
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6answers
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Should I go for a 'balanced' dataset or a 'representative' dataset?

My 'machine learning' task is of separating benign Internet traffic from malicious traffic. In the real world scenario, most (say 90% or more) of Internet traffic is benign. Thus I felt that I should ...
145
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6answers
161k views

The cross-entropy error function in neural networks

In the MNIST For ML Beginners they define cross-entropy as $$H_{y'} (y) := - \sum_{i} y_{i}' \log (y_i)$$ $y_i$ is the predicted probability value for class $i$ and $y_i'$ is the true probability ...
51
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9answers
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Why should the data be shuffled for machine learning tasks

In machine learning tasks it is common to shuffle data and normalize it. The purpose of normalization is clear (for having same range of feature values). But, after struggling a lot, I did not find ...
34
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4answers
15k views

Quick guide into training highly imbalanced data sets

I have a classification problem with approximately 1000 positive and 10000 negative samples in training set. So this data set is quite unbalanced. Plain random forest is just trying to mark all test ...
33
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2answers
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Should we apply normalization to test data as well?

I am doing a project on author identification problem. I had applied the tf-idf normalization to train data and then trained a svm on that data. Now when using the classifier should I normalize test ...
23
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2answers
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Why do we need to discard one dummy variable?

I have learned that, for creating a regression model, we have to take care of categorical variables by converting them into dummy variables. As an example, if, in our data set, there is a variable ...
8
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4answers
6k views

Why decision tree needs categorical variable to be encoded?

As per my intuition, decision trees should work better with categorical variables than with continuous variables. If this is the case, why is encoding needed on categorical variables? Can someone give ...
26
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3answers
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Data Science Project Ideas [closed]

I don't know if this is a right place to ask this question, but a community dedicated to Data Science should be the most appropriate place in my opinion. I have just started with Data Science and ...
28
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4answers
11k views

Role derivative of sigmoid function in neural networks

I try to understand role of derivative of sigmoid function in neural networks. First I plot sigmoid function, and derivative of all points from definition using python. What is the role of this ...
17
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5answers
4k views

Beginner math books for Machine Learning

I'm a Computer Science engineer with no background in statistics or advanced math. I'm studying the book Python Machine Learning by Raschka and Mirjalili, but when I tried to understand the math of ...
19
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1answer
33k views

Why ReLU is better than the other activation functions

Here the answer refers to vanishing and exploding gradients that has been in sigmoid-like activation functions but, I guess, Relu...
31
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2answers
30k views

Why use both validation set and test set?

Consider a neural network: For a given set of data, we divide it into training, validation and test set. Suppose we do it in the classic 60:20:20 ratio, then we prevent overfitting by validating the ...
12
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1answer
14k views

How to Predict the future values of time horizon with Keras?

I just built this LSTM neural network with Keras ...
6
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4answers
7k views

How to give name to topics created using LDA?

I have categorized 800,000 documents into 500 categories using the Mahout topic modelling. Instead of representing the topic using the top 5/10 words for each topics, I want to infer a generic name ...
9
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3answers
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How to plot logistic regression decision boundary?

I am running logistic regression on a small dataset which looks like this: After implementing gradient descent and the cost function, I am getting a 100% accuracy in the prediction stage, However I ...
60
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10answers
62k views

Machine learning - features engineering from date/time data

What are the common/best practices to handle time data for machine learning application? For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can ...
48
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2answers
50k views

How to interpret the output of XGBoost importance?

I ran a xgboost model. I don't exactly know how to interpret the output of xgb.importance. What is the meaning of Gain, Cover, and Frequency and how do we ...
39
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3answers
44k views

StandardScaler before and after splitting data

When I was reading about using StandardScaler, most of the recommendations were saying that you should use StandardScaler before ...
23
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1answer
24k views

back propagation in CNN

I have the following CNN: I start with an input image of size 5x5 Then I apply convolution using 2x2 kernel and stride = 1, that produces feature map of size 4x4. Then I apply 2x2 max-pooling with ...
19
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3answers
8k views

Linear regression with non-symmetric cost function?

I want to predict some value $Y(x)$ and I am trying to get some prediction $\hat Y(x)$ that optimizes between being as low as possible, but still being larger than $Y(x)$. In other words: $$\text{cost}...
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2answers
7k views

What are useful evaluation metrics used in machine learning

I am using CNN in order to predict codes after analyzing text. As an example, I will write "I am crazy" .. the model will predict some code " X321". All this based on CNN. I want to evaluate my ...
8
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1answer
16k views

Why should softmax be used in CNN

In the last layer of CNNs and MLPs it is common to use softmax layer or units with sigmoid activation functions for multi-class ...
6
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3answers
848 views

Correlation vs Multicollinearity

I have been taught to check correlation matrix before going for any algorithm. I have a few questions around the same: Pearson Correlation is for numerical variables only. What if we have to check ...
11
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4answers
9k views

Overfitting/Underfitting with Data set size

In the below graph, x-axis => Data set Size y-axis => Cross validation Score Red line is for Training Data Green line is for Testing Data In a tutorial that I'm referring to, the author says ...
4
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2answers
55 views

Machine Learning with intended missing values

I have a dataset relating to humans completing reviews, the target variable is whether the review decision is correct / incorrect and one of my features is a trailing 4 week accuracy score for the ...
3
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1answer
180 views

Understanding computations of Perceptron and Multi-Layer Perceptrons on Geometric level

I am currently watching amazing Deep Learning lecture series from Carnegie Melllon University, but I am having little bit of trouble understanding how Perceptrons and MLP are making their decisions on ...
9
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1answer
3k views

Can The linearly non-separable data be learned using polynomial features with logistic regression?

I know that Polynomial Logistic Regression can easily learn a typical data like the following image: I was wondering whether the following two data also can be ...
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1answer
240 views

Why to adjust class weights instead of simply finding the best threshold?

In a binary supervised classification where classes 1 and 0 have different number of samples in training, it’s very common to find tutorials about adjusting class weights, over and under sampling for ...
149
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17answers
124k views

Best python library for neural networks

I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?
141
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18answers
159k views

How do you visualize neural network architectures?

When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture. What are good / simple ways to visualize common ...
68
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7answers
130k views

Cross-entropy loss explanation

Suppose I build a neural network for classification. The last layer is a dense layer with Softmax activation. I have five different classes to classify. Suppose for a single training example, the <...
27
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6answers
9k views

Deep learning basics

I am looking for a paper detailing the very basics of deep learning. Ideally like the Andrew Ng course for deep learning. Do you know where I can find this ?
54
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3answers
96k views

How to set batch_size, steps_per epoch, and validation steps?

I am starting to learn CNNs using Keras. I am using the theano backend. I don't understand how to set values to: batch_size ...
40
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9answers
22k views

Why are Machine Learning models called black boxes?

I was reading this blog post titled: The Financial World Wants to Open AI’s Black Boxes, where the author repeatedly refer to ML models as "black boxes". A similar terminology has been used at ...
39
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3answers
24k views

Why is ReLU used as an activation function?

Activation functions are used to introduce non-linearities in the linear output of the type w * x + b in a neural network. Which I am able to understand ...
16
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2answers
11k views

High-dimensional data: What are useful techniques to know?

Due to various curses of dimensionality, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/...
28
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7answers
14k views

Can machine learning learn a function like finding maximum from a list?

I have an input which is a list and the output is the maximum of the elements of the input-list. Can machine learning learn such a function which always selects the maximum of the input-elements ...
40
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6answers
29k views

Encoding features like month and hour as categorial or numeric?

Is it better to encode features like month and hour as factor or numeric in a machine learning model? On the one hand, I feel numeric encoding might be reasonable, because time is a forward ...
6
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2answers
13k views

Python or R for implementing machine learning algorithms for fraud detection [closed]

I was wondering which language can I use: R or Python, for my internship in fraud detection in an online banking system: I have to build machine learning algorithms (NN, etc.) that predict transaction ...
15
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3answers
4k views

Can a neural network compute $y = x^2$?

In spirit of the famous Tensorflow Fizz Buzz joke and XOr problem I started to think, if it's possible to design a neural network that implements $y = x^2$ function? Given some representation of a ...
10
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4answers
1k views

What initial steps should I use to make sense of large data sets, and what tools should I use?

Caveat: I am a complete beginner when it comes to machine learning, but eager to learn. I have a large dataset and I'm trying to find pattern in it. There may / may not be correlation across the data,...

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