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.
11,306
questions
44
votes
6
answers
38k
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 ...
- 585
43
votes
13
answers
27k
views
Data science related funny quotes [closed]
It has been customary for the users of different communities to quote funny things about their fields. It may be fun to share your funny things about Machine Learning, Deep Learning, Data Science and ...
42
votes
9
answers
24k
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 ...
- 8,256
42
votes
10
answers
46k
views
Can machine learning algorithms predict sports scores or plays?
I have a variety of NFL datasets that I think might make a good side-project, but I haven't done anything with them just yet.
Coming to this site made me think of machine learning algorithms and I ...
- 3,128
42
votes
1
answer
36k
views
How to decide neural network architecture?
I was wondering how do we have to decide how many nodes in hidden layers, and how many hidden layers to put when we build a neural network architecture.
I understand the input and output layer ...
- 535
42
votes
6
answers
67k
views
When would one use Manhattan distance as opposed to Euclidean distance?
I am trying to look for a good argument on why one would use the Manhattan distance over the Euclidean distance in machine learning.
The closest thing I found to a good argument so far is on this MIT ...
41
votes
2
answers
52k
views
How to calculate mAP for detection task for the PASCAL VOC Challenge?
How to calculate the mAP (mean Average Precision) for the detection task for the Pascal VOC leaderboards?
There said - at page 11:
Average Precision (AP). For the VOC2007 challenge, the interpolated
...
- 649
40
votes
6
answers
52k
views
Are there free cloud services to train machine learning models?
I want to train a deep model with a large amount of training data, but my desktop does not have that power to train such a deep model with these abundant data.
I'd like to know whether there are any ...
- 13.9k
40
votes
5
answers
11k
views
What are some standard ways of computing the distance between documents?
When I say "document", I have in mind web pages like Wikipedia articles and news stories. I prefer answers giving either vanilla lexical distance metrics or state-of-the-art semantic distance metrics,...
- 821
40
votes
6
answers
11k
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 ...
- 573
40
votes
4
answers
37k
views
Why do we need XGBoost and Random Forest?
I wasn't clear on couple of concepts:
XGBoost converts weak learners to strong learners. What's the advantage of doing this ? Combining many weak learners instead of just using a single tree ?
...
- 697
40
votes
1
answer
21k
views
The difference between `Dense` and `TimeDistributedDense` of `Keras`
I am still confused about the difference between Dense and TimeDistributedDense of Keras ...
- 503
40
votes
3
answers
3k
views
When to use what - Machine Learning [closed]
Recently in a Machine Learning class from professor Oriol Pujol at UPC/Barcelona he described the most common algorithms, principles and concepts to use for a wide range of machine learning related ...
- 1,490
39
votes
3
answers
35k
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 ...
- 1,346
39
votes
5
answers
101k
views
When to use Random Forest over SVM and vice versa?
When would one use Random Forest over SVM and vice versa?
I understand that ...
- 545
38
votes
4
answers
56k
views
Do Random Forest overfit?
I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when ...
- 595
38
votes
2
answers
102k
views
Keras difference beetween val_loss and loss during training
What is the difference between val_loss and loss during training in Keras?
E.g.
...
37
votes
5
answers
48k
views
In the context of Deep Learning, what is training warmup steps
I found the term "training warmup steps" in some of the papers. What exactly does this term mean? Has it got anything to do with "learning rate"? If so, how does it affect it?
- 1,059
37
votes
5
answers
57k
views
Are decision tree algorithms linear or nonlinear
Recently a friend of mine was asked whether decision tree algorithms are linear or nonlinear algorithms in an interview. I tried to look for answers to this question but couldn't find any satisfactory ...
- 511
37
votes
4
answers
20k
views
Meaning of latent features?
I am learning about matrix factorization for recommender systems and I am seeing the term latent features occurring too frequently but I am unable to understand ...
- 719
37
votes
2
answers
83k
views
How to use the output of GridSearch?
I'm currently working with Python and Scikit learn for classification purposes, and doing some reading around GridSearch I thought this was a great way for optimising my estimator parameters to get ...
- 1,732
37
votes
1
answer
36k
views
RNN's with multiple features
I have a bit of self taught knowledge working with Machine Learning algorithms (the basic Random Forest and Linear Regression type stuff). I decided to branch out and begin learning RNN's with Keras. ...
- 1,205
35
votes
9
answers
13k
views
Why is it wrong to train and test a model on the same dataset?
What are the pitfalls of doing so and why is it a bad practice? Is it possible that the model starts to learn the images "by heart" instead of understanding the underlying logic?
- 461
35
votes
4
answers
16k
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 ...
- 5,454
35
votes
6
answers
16k
views
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 ...
- 451
34
votes
4
answers
75k
views
How to use LeakyRelu as activation function in sequence DNN in keras?When it perfoms better than Relu?
How do you use LeakyRelu as an activation function in sequence DNN in keras?
If I want to write something similar to:
...
- 1,824
33
votes
4
answers
34k
views
When to use cosine simlarity over Euclidean similarity
In NLP, people tend to use cosine similarity to measure document/text distances. I want to hear what do people think of the following two scenarios, which to pick, cosine similarity or Euclidean?
...
- 453
33
votes
1
answer
27k
views
Ways to deal with longitude/latitude feature [closed]
I am working on a fictional dataset with 25 features. Two of the features are latitude and longitude of a place and others are pH values, elevation, windSpeed etc with varying ranges. I can perform ...
- 443
32
votes
6
answers
118k
views
Validation loss is not decreasing
I am trying to train a LSTM model. Is this model suffering from overfitting?
Here is train and validation loss graph:
- 571
32
votes
3
answers
134k
views
How can I check the correlation between features and target variable?
I am trying to build a Regression model and I am looking for a way to check whether there's any correlation between features and target variables?
This is my ...
- 931
32
votes
4
answers
13k
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 ...
- 467
31
votes
6
answers
24k
views
What is the reason behind taking log transformation of few continuous variables?
I have been doing a classification problem and I have read many people's code and tutorials. One thing I've noticed is that many people take np.log or ...
- 611
31
votes
4
answers
30k
views
What algorithms should I use to perform job classification based on resume data?
Note that I am doing everything in R.
The problem goes as follow:
Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don't. The goal here is to: ...
- 431
31
votes
2
answers
53k
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?
- 931
31
votes
1
answer
26k
views
What is a LB score in machine learning?
I was going through an article on kaggle blogs. Repeatedly, the author mentions 'LB score' and 'LB fit') as a metric for effectiveness of machine learning (along with cross validation (CV) score).
...
- 505
31
votes
3
answers
33k
views
General approach to extract key text from sentence (nlp)
Given a sentence like:
Complimentary gym access for two for the length of stay ($12 value per person per day)
What general approach can I take to identify the ...
- 421
30
votes
7
answers
18k
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 ...
- 309
30
votes
8
answers
3k
views
Purpose of visualizing high dimensional data?
There are many techniques for visualizing high dimension datasets, such as T-SNE, isomap, PCA, supervised PCA, etc. And we go through the motions of projecting the data down to a 2D or 3D space, so we ...
- 675
29
votes
6
answers
10k
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 ?
- 433
29
votes
7
answers
20k
views
Difference between AlphaGo's policy network and value network
I was reading a high level summary about Google's AlphaGo, and I came across the terms "policy network" and "value network". At a high level, I understand that the policy network ...
- 4,139
29
votes
2
answers
23k
views
When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?
In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer.
What are some situations to use L1,L2 regularization instead of dropout layer? What are some situations ...
- 1,335
29
votes
1
answer
36k
views
Word2Vec vs. Sentence2Vec vs. Doc2Vec
I recently came across the terms Word2Vec, Sentence2Vec and Doc2Vec and kind of confused as I am new to vector semantics. Can someone please elaborate the differences in these methods in simple words. ...
- 529
29
votes
1
answer
19k
views
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? ...
- 665
28
votes
3
answers
68k
views
What does "baseline" mean in the context of machine learning?
What does "baseline" mean in the context of machine learning and data science?
Someone wrote me:
Hint: An appropriate baseline will give an RMSE of approximately 200.
I don't get this. Does he ...
- 411
28
votes
3
answers
55k
views
What is weight and bias in deep learning?
I'm starting to learn Machine learning from Tensorflow website. I have developed a very very rudimentary understanding of the flow a deep learning program follows (this method makes me learn fast ...
- 389
28
votes
3
answers
43k
views
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 ...
- 383
28
votes
6
answers
3k
views
Machine learning techniques for estimating users' age based on Facebook sites they like
I have a database from my Facebook application and I am trying to use machine learning to estimate users' age based on what Facebook sites they like.
There are three crucial characteristics of my ...
- 916
28
votes
3
answers
2k
views
Why are NLP and Machine Learning communities interested in deep learning?
I hope you can help me, as I have some questions on this topic. I'm new in the field of deep learning, and while I did some tutorials, I can't relate or distinguish concepts from one another.
- 449
28
votes
2
answers
18k
views
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)...
- 393
28
votes
4
answers
23k
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 ...
- 455