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289 votes
8 answers
374k views

Micro Average vs Macro average Performance in a Multiclass classification setting

I am trying out a multiclass classification setting with 3 classes. The class distribution is skewed with most of the data falling in 1 of the 3 classes. (class labels being 1,2,3, with 67.28% of the ...
SHASHANK GUPTA's user avatar
283 votes
12 answers
277k views

What are deconvolutional layers?

I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The ...
Martin Thoma's user avatar
  • 18.9k
263 votes
10 answers
430k views

How to set class weights for imbalanced classes in Keras?

I know that there is a possibility in Keras with the class_weights parameter dictionary at fitting, but I couldn't find any example. Would somebody so kind to ...
Hendrik's user avatar
  • 8,607
240 votes
10 answers
351k views

What's the difference between fit and fit_transform in scikit-learn models?

I do not understand the difference between the fit and fit_transform methods in scikit-learn. Can anybody explain simply why we ...
Kaggle's user avatar
  • 2,877
202 votes
35 answers
33k views

Publicly Available Datasets

One of the common problems in data science is gathering data from various sources in a somehow cleaned (semi-structured) format and combining metrics from various sources for making a higher level ...
202 votes
13 answers
308k views

K-Means clustering for mixed numeric and categorical data

My data set contains a number of numeric attributes and one categorical. Say, NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr, where ...
IgorS's user avatar
  • 5,474
200 votes
17 answers
415k views

Train/Test/Validation Set Splitting in Sklearn

How could I randomly split a data matrix and the corresponding label vector into a X_train, X_test, ...
Hendrik's user avatar
  • 8,607
198 votes
5 answers
152k 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 ...
tejaskhot's user avatar
  • 4,065
198 votes
6 answers
365k views

How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
Muhammad Ali's user avatar
  • 2,487
194 votes
2 answers
240k views

Difference between isna() and isnull() in pandas

I have been using pandas for quite some time. But, I don't understand what's the difference between isna() and isnull(). And, ...
Vaibhav Thakur's user avatar
180 votes
6 answers
183k views

When to use GRU over LSTM?

The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU ...
Sayali Sonawane's user avatar
178 votes
21 answers
252k 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 ...
Martin Thoma's user avatar
  • 18.9k
172 votes
4 answers
124k views

When to use One Hot Encoding vs LabelEncoder vs DictVectorizor?

I have been building models with categorical data for a while now and when in this situation I basically default to using scikit-learn's LabelEncoder function to transform this data prior to building ...
anthr's user avatar
  • 1,843
152 votes
6 answers
166k 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 ...
Martin Thoma's user avatar
  • 18.9k
150 votes
17 answers
126k 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?
147 votes
13 answers
98k views

Why do people prefer Pandas to SQL?

I've been using SQL since 1996, so I may be biased. I've used MySQL and SQLite 3 extensively, but have also used Microsoft SQL Server and Oracle. The vast majority of the operations I've seen done ...
132 votes
1 answer
380k views

How to get correlation between two categorical variable and a categorical variable and continuous variable?

I am building a regression model and I need to calculate the below to check for correlations Correlation between 2 Multi level categorical variables Correlation between a Multi level categorical ...
GeorgeOfTheRF's user avatar
127 votes
14 answers
120k views

Python vs R for machine learning

I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I have seen people using Python. What ...
124 votes
2 answers
115k views

Training an RNN with examples of different lengths in Keras

I am trying to get started learning about RNNs and I'm using Keras. I understand the basic premise of vanilla RNN and LSTM layers, but I'm having trouble understanding a certain technical point for ...
Tac-Tics's user avatar
  • 1,360
117 votes
12 answers
152k views

SVM using scikit learn runs endlessly and never completes execution

I am trying to run SVR using scikit-learn (python) on a training dataset that has 595605 rows and 5 columns (features) while the test dataset has 397070 rows. The data has been pre-processed and ...
tejaskhot's user avatar
  • 4,065
115 votes
5 answers
71k views

Why do cost functions use the square error?

I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable. I have learnt that there is a hypothesis, which is: $h_\theta(x)=\...
Golo Roden's user avatar
  • 1,323
114 votes
11 answers
126k views

Choosing a learning rate

I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...
ragingSloth's user avatar
  • 1,824
112 votes
9 answers
139k views

When should I use Gini Impurity as opposed to Information Gain (Entropy)?

Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)? Which metric is better to use in different scenarios while using decision trees?
Krish Mahajan's user avatar
111 votes
5 answers
83k views

Backprop Through Max-Pooling Layers?

This is a small conceptual question that's been nagging me for a while: How can we back-propagate through a max-pooling layer in a neural network? I came across max-pooling layers while going through ...
shinvu's user avatar
  • 1,240
104 votes
4 answers
109k views

What is the positional encoding in the transformer model?

I'm trying to read and understand the paper Attention is all you need and in it, there is a picture: I don't know what positional encoding is. by listening to some youtube videos I've found out that ...
Peyman's user avatar
  • 1,143
98 votes
4 answers
104k views

Advantages of AUC vs standard accuracy

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...
aidankmcl's user avatar
  • 1,083
95 votes
10 answers
441k views

ValueError: Input contains NaN, infinity or a value too large for dtype('float32')

I got ValueError when predicting test data using a RandomForest model. My code: ...
Edamame's user avatar
  • 2,755
94 votes
12 answers
20k views

How big is big data?

Lots of people use the term big data in a rather commercial way, as a means of indicating that large datasets are involved in the computation, and therefore potential solutions must have good ...
Rubens's user avatar
  • 4,107
90 votes
7 answers
121k views

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, ...
Spider's user avatar
  • 1,279
89 votes
1 answer
88k views

When to use (He or Glorot) normal initialization over uniform init? And what are its effects with Batch Normalization?

I knew that Residual Network (ResNet) made He normal initialization popular. In ResNet, He normal initialization is used , while the first layer uses He uniform initialization. I've looked through ...
Rizky Luthfianto's user avatar
87 votes
6 answers
151k views

strings as features in decision tree/random forest

I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-...
user3001408's user avatar
  • 1,005
87 votes
4 answers
52k views

How are 1x1 convolutions the same as a fully connected layer?

I recently read Yan LeCuns comment on 1x1 convolutions: In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with 1x1 convolution ...
Martin Thoma's user avatar
  • 18.9k
86 votes
8 answers
66k views

Time series prediction using ARIMA vs LSTM

The problem that I am dealing with is predicting time series values. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values....
ahajib's user avatar
  • 1,075
84 votes
6 answers
81k views

Cosine similarity versus dot product as distance metrics

It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? ...
ahoffer's user avatar
  • 943
83 votes
5 answers
48k views

What is the difference between "equivariant to translation" and "invariant to translation"

I'm having trouble understanding the difference between equivariant to translation and invariant to translation. In the book Deep Learning. MIT Press, 2016 (I. Goodfellow, A. Courville, and Y. Bengio)...
Aamir 's user avatar
  • 993
83 votes
5 answers
125k views

GBM vs XGBOOST? Key differences?

I am trying to understand the key differences between GBM and XGBOOST. I tried to google it, but could not find any good answers explaining the differences between the two algorithms and why xgboost ...
Aman 's user avatar
  • 997
81 votes
9 answers
32k views

Data scientist vs machine learning engineer

What are the differences, if any, between a "data scientist" and a "machine learning engineer"? Over the past year or so "machine learning engineer" has started to show up a lot in job postings. ...
Ryan Zotti's user avatar
  • 4,149
78 votes
6 answers
162k views

What is the difference between Gradient Descent and Stochastic Gradient Descent?

What is the difference between Gradient Descent and Stochastic Gradient Descent? I am not very familiar with these, can you describe the difference with a short example?
Developer's user avatar
  • 1,099
77 votes
10 answers
194k views

How to clone Python working environment on another machine?

I developed a machine learning model with Python (Anaconda + Flask) on my workstation and all goes well. Later, I tried to ship this program onto another machine where of course I tried to set up the ...
Hendrik's user avatar
  • 8,607
77 votes
4 answers
218k views

Convert a list of lists into a Pandas Dataframe

I am trying to convert a list of lists which looks like the following into a Pandas Dataframe ...
Aravind Veluchamy's user avatar
75 votes
6 answers
150k 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 <...
enterML's user avatar
  • 3,031
73 votes
7 answers
83k views

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, ...
ximiki's user avatar
  • 933
71 votes
4 answers
90k views

What is purpose of the [CLS] token and why is its encoding output important?

I am reading this article on how to use BERT by Jay Alammar and I understand things up until: For sentence classification, we’re only only interested in BERT’s output for the [CLS] token, so we ...
user3768495's user avatar
71 votes
2 answers
11k views

Are Support Vector Machines still considered "state of the art" in their niche?

This question is in response to a comment I saw on another question. The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much ...
Neil Slater's user avatar
  • 28.9k
70 votes
11 answers
40k views

What is dimensionality reduction? What is the difference between feature selection and extraction?

From wikipedia: dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature ...
alvas's user avatar
  • 2,410
69 votes
11 answers
102k views

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 ...
Green Falcon's user avatar
  • 14.1k
69 votes
4 answers
167k views

What is the use of torch.no_grad in pytorch?

I am new to pytorch and started with this github code. I do not understand the comment in line 60-61 in the code ...
mausamsion's user avatar
  • 1,282
69 votes
5 answers
51k views

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 ...
Rjay155's user avatar
  • 1,215
68 votes
6 answers
82k views

What is the Q function and what is the V function in reinforcement learning?

It seems to me that the $V$ function can be easily expressed by the $Q$ function and thus the $V$ function seems to be superfluous to me. However, I'm new to reinforcement learning so I guess I got ...
Martin Thoma's user avatar
  • 18.9k
67 votes
2 answers
65k views

Sparse_categorical_crossentropy vs categorical_crossentropy (keras, accuracy)

Which is better for accuracy or are they the same? Of course, if you use categorical_crossentropy you use one hot encoding, and if you use ...
Master M's user avatar
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