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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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9 answers
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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 ...
Dawny33's user avatar
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44 votes
6 answers
40k 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 ...
Funkwecker's user avatar
44 votes
6 answers
72k 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 ...
Bitcoin Cash - ADA enthusiast's user avatar
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 ...
43 votes
5 answers
59k 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?
Ashwin Geet D'Sa's user avatar
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 ...
Steve Kallestad's user avatar
42 votes
1 answer
40k 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 ...
user7677413's user avatar
41 votes
6 answers
54k 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 ...
Green Falcon's user avatar
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41 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 ...
stk1234's user avatar
  • 583
41 votes
5 answers
106k 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 ...
Rohit's user avatar
  • 565
41 votes
2 answers
53k 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 ...
Alex's user avatar
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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,...
Matt's user avatar
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40 votes
3 answers
37k 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 ...
user1825567's user avatar
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40 votes
4 answers
38k 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 ? ...
John Constantine's user avatar
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 ...
fluency03's user avatar
  • 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 ...
Javierfdr's user avatar
  • 1,490
39 votes
2 answers
107k 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. ...
Vladimir Shebuniayeu's user avatar
38 votes
4 answers
58k 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 ...
papafe's user avatar
  • 595
38 votes
5 answers
59k 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 ...
user2966197's user avatar
37 votes
4 answers
21k 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 ...
Jack Twain's user avatar
37 votes
2 answers
85k 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 ...
Dan Carter's user avatar
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37 votes
1 answer
37k 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. ...
Rjay155's user avatar
  • 1,215
36 votes
6 answers
17k 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 ...
Praise the lord's user avatar
35 votes
9 answers
15k 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?
karalis1's user avatar
  • 461
35 votes
6 answers
127k 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:
DukeLover's user avatar
  • 601
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 ...
IgorS's user avatar
  • 5,474
35 votes
4 answers
81k 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: ...
user10296606's user avatar
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34 votes
4 answers
40k 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? ...
Logan's user avatar
  • 463
33 votes
1 answer
28k 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 ...
AllThingsScience's user avatar
33 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 ...
lukassz's user avatar
  • 477
32 votes
2 answers
57k 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?
Taimur Islam's user avatar
32 votes
3 answers
142k 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 ...
Anjith's user avatar
  • 951
32 votes
2 answers
25k 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 ...
user781486's user avatar
  • 1,425
31 votes
7 answers
19k 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 ...
user78739's user avatar
  • 319
31 votes
6 answers
26k 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 ...
Sai Kumar's user avatar
  • 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: ...
user1769197's user avatar
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). ...
user345394's user avatar
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 ...
William Falcon's user avatar
30 votes
8 answers
4k 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 ...
hlin117's user avatar
  • 685
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 ?
Maxi's user avatar
  • 433
29 votes
7 answers
21k 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 ...
Ryan Zotti's user avatar
  • 4,149
29 votes
4 answers
26k 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 ...
NaveganTeX's user avatar
29 votes
1 answer
37k 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. ...
Smith's user avatar
  • 529
29 votes
1 answer
20k 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? ...
Smith Volka's user avatar
28 votes
3 answers
71k 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 ...
Meiiso's user avatar
  • 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 ...
Umer Farooq's user avatar
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 ...
Kevin Desai's user avatar
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 ...
Wojciech Walczak's user avatar
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.
user3352632's user avatar
28 votes
3 answers
37k views

What is the exact definition of VC dimension?

I'm studying machine learning from Andrew Ng Stanford lectures and just came across the theory of VC dimensions. According to the lectures and what I understood, the definition of VC dimension can be ...
Kaushal28's user avatar
  • 383