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202 votes
13 answers

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
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 ...
stk1234's user avatar
  • 583
132 votes
1 answer

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
47 votes
5 answers

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 ...
wit221's user avatar
  • 573
265 votes
10 answers

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
  • 8,677
36 votes
6 answers

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
91 votes
7 answers

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
  • 1,289
283 votes
12 answers

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
202 votes
35 answers

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 ...
73 votes
7 answers

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
290 votes
8 answers

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
199 votes
5 answers

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
  • 4,085
57 votes
5 answers

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, ...
pcko1's user avatar
  • 3,950
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 ...
EitanT's user avatar
  • 519
10 votes
1 answer

Why you shouldn't upsample before cross validation

I have an imbalanced dataset and I am trying different methods to address the data imbalance. I found this article that explains the correct way to cross-validate when oversampling data using SMOTE ...
sums22's user avatar
  • 437
5 votes
3 answers

Obtaining consistent one-hot encoding of train / production data

I'm building an app that will require user input. Currently, on the training set, I run the following code, in which data is a pandas dataframe with a combination ...
Andrew Maurer's user avatar
241 votes
10 answers

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
  • 2,887
70 votes
5 answers

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
  • 1,225
31 votes
2 answers

How to feed LSTM with different input array sizes?

If I like to write a LSTM network and feed it by different input array sizes, how is it possible? For example I want to get voice messages or text messages in a ...
user3486308's user avatar
  • 1,300
21 votes
4 answers

Train/Test Split after performing SMOTE

I am dealing with a highly unbalanced dataset so I used SMOTE to resample it. After SMOTE resampling, I split the resampled dataset into training/test sets using the training set to build a model and ...
Edamame's user avatar
  • 2,765
5 votes
1 answer

How pre-trained BERT model generates word embeddings for out of vocabulary words?

Currently, I am reading BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. I want to understand how pre-trained BERT generates word embeddings for out of vocabulary ...
Sayali Sonawane's user avatar
3 votes
4 answers

Is it possible to get worse model after optimization?

I am trying recently to optimize models but for some reason, whenever I try to run the optimization the model score in the end is worse than before, so I believe I do something wrong. in order to ...
Reut's user avatar
  • 299
174 votes
4 answers

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
  • 1,863
95 votes
10 answers

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
  • 2,765
69 votes
4 answers

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....
Hendrik's user avatar
  • 8,677
59 votes
6 answers

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 ...
pnp's user avatar
  • 693
45 votes
2 answers

Should we apply normalization to test data as well?

I am doing a project on an author identification problem. I applied the tf-idf normalization to train data and then trained an SVM on that data. Now when using the classifier, should I normalize test ...
Kishan Kumar's user avatar
44 votes
6 answers

What is the relationship between the accuracy and the loss in deep learning?

I have created three different models using deep learning for multi-class classification and each model gave me a different accuracy and loss value. The results of the testing model as the following: ...
N.IT's user avatar
  • 2,005
24 votes
3 answers

Starting my career as Data Scientist, is Software Engineering experience required? [closed]

I am an MSc student at the University of Edinburgh, specialized in machine learning and natural language processing. I had some practical courses focused on data mining, and others dealing with ...
cpumar's user avatar
  • 807
16 votes
1 answer

Multi-dimentional and multivariate Time-Series forecast (RNN/LSTM) Keras

I have been trying to understand how to represent and shape data to make a multidimentional and multivariate time series forecast using Keras (or TensorFlow) but I am still very unclear after reading ...
Bastien's user avatar
  • 263
16 votes
5 answers

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 ...
10 votes
5 answers

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 ...
Mukesh K's user avatar
  • 101
5 votes
1 answer

How to preprocess with NLP a big dataset for text classification

TL;DR I've never done nlp before and I feel like I'm not doing it in the good way. I'd like to know if I'm really doing things in a bad way since the beginning or ...
gabriel garcia's user avatar
3 votes
1 answer

How to interpret Variance Inflation Factor (VIF) results?

From various books and blog posts, I understood that the Variance Inflation Factor (VIF) is used to calculate collinearity. They say that VIF till 10 is good. But I have a question. As we can see in ...
thewhitetulip's user avatar
2 votes
1 answer

How to efficiently iterate a supervised model over the Cartesian product of very large number of records?

The problem: Two large databases, with ~1M records each, "old customer data" and "new customer data". The data came from different sources and was ingested at different times, so there are many ...
Alex S Kinman's user avatar
152 votes
6 answers

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
69 votes
11 answers

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
63 votes
9 answers

Tools and protocol for reproducible data science using Python

I am working on a data science project using Python. The project has several stages. Each stage comprises of taking a data set, using Python scripts, auxiliary data, configuration and parameters, and ...
Yuval F's user avatar
  • 761
59 votes
6 answers

Does XGBoost handle multicollinearity by itself?

I'm currently using XGBoost on a data-set with 21 features (selected from list of some 150 features), then one-hot coded them to obtain ~98 features. A few of these 98 features are somewhat redundant, ...
neural-nut's user avatar
  • 1,783
51 votes
3 answers

StandardScaler before or after splitting data - which is better?

When I was reading about using StandardScaler, most of the recommendations were saying that you should use StandardScaler before ...
tsumaranaina's user avatar
44 votes
9 answers

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
  • 8,296
42 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 ...
shda's user avatar
  • 585
35 votes
4 answers

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
26 votes
2 answers

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 ...
Mithun Sarker Shuvro's user avatar
23 votes
2 answers

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...
Green Falcon's user avatar
  • 14.1k
18 votes
4 answers

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 ...
Boris Burkov's user avatar
6 votes
3 answers

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 ...
Payal Bhatia's user avatar
5 votes
2 answers

Encode multi-class response variable

In a classification problem when the response variable has multi-class, e.g., "sunny","rainy","cloudy", how should we encode it? I know that for predictors like this, usually we do One Hot Encoding, ...
KevinKim's user avatar
  • 635
182 votes
21 answers

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
125 votes
2 answers

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
  • 1,370

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