Data Science Stack Exchange
Data Science Stack Exchange

Most Popular

more tags
1500 questions
42
votes
1 answer

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 depends on the training set that we have but how do we…
user7677413
  • 535
  • 1
  • 4
  • 5
42
votes
4 answers

Guidelines for selecting an optimizer for training neural networks

I have been using neural networks for a while now. However, one thing that I constantly struggle with is the selection of an optimizer for training the network (using backprop). What I usually do is just start with one (e.g. standard SGD) and then…
mplappert
  • 521
  • 1
  • 4
  • 4
41
votes
5 answers

When to use Random Forest over SVM and vice versa?

When would one use Random Forest over SVM and vice versa? I understand that cross-validation and model comparison is an important aspect of choosing a model, but here I would like to learn more about rules of thumb and heuristics of the two…
Rohit
  • 565
  • 1
  • 5
  • 7
41
votes
8 answers

What would I prefer - an over-fitted model or a less accurate model?

Let's say we have two models trained. And let's say we are looking for good accuracy. The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an accuracy of 83% on training set and 83% on test set.…
EitanT
  • 519
  • 4
  • 3
41
votes
1 answer

Pearson vs Spearman vs Kendall

What are the characteristics of the three correlation coefficients and what are the comparisons of each of them/assumptions? Can somebody kindly take me through the concepts?
user86099
41
votes
5 answers

Data normalization before or after train-test split?

Which one is the right approach to make data normalization - before or after train-test split? Normalization before split from sklearn.preprocessing import StandardScaler normalized_X_features = pd.DataFrame( …
Tauno
  • 799
  • 2
  • 9
  • 9
41
votes
2 answers

How to prepare/augment images for neural network?

I would like to use a neural network for image classification. I'll start with pre-trained CaffeNet and train it for my application. How should I prepare the input images? In this case, all the images are of the same object but with variations…
Alex I
  • 3,152
  • 1
  • 21
  • 27
41
votes
6 answers

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 the number of neurons in each middle layer? Is it…
stk1234
  • 583
  • 1
  • 6
  • 6
41
votes
2 answers

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 average precision (Salton and Mcgill 1986) was…
Alex
  • 649
  • 1
  • 7
  • 13
41
votes
6 answers

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 free cloud services that can be used for training…
Green Falcon
  • 14,058
  • 9
  • 57
  • 98
41
votes
3 answers

Choosing between CPU and GPU for training a neural network

I've seen discussions about the 'overhead' of a GPU, and that for 'small' networks, it may actually be faster to train on a CPU (or network of CPUs) than a GPU. What is meant by 'small'? For example, would a single-layer MLP with 100 hidden units…
StatsSorceress
  • 2,001
  • 3
  • 14
  • 30
41
votes
6 answers

Unbalanced multiclass data with XGBoost

I have 3 classes with this distribution: Class 0: 0.1169 Class 1: 0.7668 Class 2: 0.1163 And I am using xgboost for classification. I know that there is a parameter called scale_pos_weight. But how is it handled for 'multiclass' case, and how can…
shda
  • 575
  • 1
  • 5
  • 10
40
votes
5 answers

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, with stronger preference for the latter.
Matt
  • 821
  • 1
  • 7
  • 12
40
votes
5 answers

Is it necessary to standardize your data before clustering?

Is it necessary to standardize your data before cluster? In the example from scikit learn about DBSCAN, here they do this in the line: X = StandardScaler().fit_transform(X) But I do not understand why it is necessary. After all, clustering does…
makansij
  • 849
  • 2
  • 12
  • 17
40
votes
3 answers

When to use what - Machine Learning

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 task. Here I share them with you and ask you: is…
Javierfdr
  • 1,490
  • 13
  • 14
1 2 3
…
99 100