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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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 ...
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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 ...
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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 ...
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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, ...
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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, ...
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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 ...
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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, ...
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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 ...
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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 ...
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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....
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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...
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Why does frequency encoding work?
Frequency encoding is a widely used technique in Kaggle competitions, and many times proves to be a very reasonable way of dealing with categorical features with high cardinality. I really don't ...
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How to Predict the future values of time horizon with Keras?
I just built this LSTM neural network with Keras
...
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How to give name to topics created using LDA?
I have categorized 800,000 documents into 500 categories using the Mahout topic modelling.
Instead of representing the topic using the top 5/10 words for each topics, I want to infer a generic name ...
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Interpreting vertical and horizontal parts of ROC curve
It's not clear to me how I can interpret vertical and horizontal parts of the ROC curve. What important information can I gain from this? This is a text from the book "Human-in-the-Loop Machine ...
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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 ...
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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?
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Machine learning - features engineering from date/time data
What are the common/best practices to handle time data for machine learning application?
For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can ...
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How to interpret the output of XGBoost importance?
I ran a xgboost model. I don't exactly know how to interpret the output of xgb.importance.
What is the meaning of Gain, Cover, and Frequency and how do we ...
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Why is ReLU used as an activation function?
Activation functions are used to introduce non-linearities in the linear output of the type w * x + b in a neural network.
Which I am able to understand ...
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back propagation in CNN
I have the following CNN:
I start with an input image of size 5x5
Then I apply convolution using 2x2 kernel and stride = 1, that produces feature map of size 4x4.
Then I apply 2x2 max-pooling with ...
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Linear regression with non-symmetric cost function?
I want to predict some value $Y(x)$ and I am trying to get some prediction $\hat Y(x)$ that optimizes between being as low as possible, but still being larger than $Y(x)$. In other words:
$$\text{cost}...
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Overfitting/Underfitting with Data set size
In the below graph,
x-axis => Data set Size
y-axis => Cross validation Score
Red line is for Training Data
Green line is for Testing Data
In a tutorial that I'm referring to, the author says ...
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Can The linearly non-separable data be learned using polynomial features with logistic regression?
I know that Polynomial Logistic Regression can easily learn a typical data like the following image:
I was wondering whether the following two data also can be ...
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Why should softmax be used in CNN
In the last layer of CNNs and MLPs it is common to use softmax layer or units with sigmoid activation functions for multi-class ...
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What are useful evaluation metrics used in machine learning
I am using CNN in order to predict codes after analyzing text. As an example, I will write "I am crazy" .. the model will predict some code " X321".
All this based on CNN.
I want to evaluate my ...
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Random Forest significantly outperforms XGBoost - problem or possible?
I have dataset of around 180k observations of 13 variables (mix of numerical and categorical features). It is binary classification problem, but classes are imbalanced (25:1 for negative ones). I ...
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Time-series multi-step generalization from single step model
I have built a generic stacked lstm model of the form:
...
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Machine Learning with intended missing values
I have a dataset relating to humans completing reviews, the target variable is whether the review decision is correct / incorrect and one of my features is a trailing 4 week accuracy score for the ...
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Understanding computations of Perceptron and Multi-Layer Perceptrons on Geometric level
I am currently watching amazing Deep Learning lecture series from Carnegie Melllon University, but I am having little bit of trouble understanding how Perceptrons and MLP are making their decisions on ...
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Why to adjust class weights instead of simply finding the best threshold?
In a binary supervised classification where classes 1 and 0 have different number of samples in training, it’s very common to find tutorials about adjusting class weights, over and under sampling for ...
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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, ...
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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-...
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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 <...
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