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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What is the difference between cross_validate and cross_val_score?
I understand cross_validate and how it works, but now I am confused about what cross_val_score actually does. Can anyone give ...
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local minima vs saddle points in deep learning
I heard Andrew Ng (in a video I unfortunately can't find anymore) talk about how the understanding of local minima in deep learning problems has changed in the sense that they are now regarded as less ...
oW_♦
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How to deal with string labels in multi-class classification with keras?
I am newbie on machine learning and keras and now working a multi-class image classification problem using keras. The input is tagged image. After some pre-processing, the training data is represented ...
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Word2Vec for Named Entity Recognition
I'm looking to use google's word2vec implementation to build a named entity recognition system. I've heard that recursive neural nets with back propagation through structure are well suited for named ...
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Is Python a viable language to do statistical analysis in?
I originally came from R, but Python seems to be the more common language these days. Ideally, I would do all my coding in Python as the syntax is easier and I've had more real life experience using ...
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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 ...
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Natural Language to SQL query
I have been working on developing a system "Converting Natural Language to SQL Query".
I have read the answers from the similar questions, but was not able to get the information that I was looking ...
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Text categorization: combining different kind of features
The problem I am tackling is categorizing short texts into multiple classes. My current approach is to use tf-idf weighted term frequencies and learn a simple linear classifier (logistic regression). ...
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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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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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What is a channel in a CNN?
I was reading an article about convolutional neural networks, and I found something that I don't understand, which is:
The filter must have the same number of channels as the input image so that the ...
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How to predict probabilities in xgboost using R?
The below predict function is giving -ve values as well so it cannot be probabilities.
...
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Why do we have to divide by 2 in the ML squared error cost function? [duplicate]
I'm not sure why you need to multiply by $\frac1{2m}$ in the beginning. I understand that you would have to divide the whole sum by $\frac1{m}$, but why do we have to multiply $m$ by two?
Is it ...
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How to make a decision tree with both continuous and categorical variables in the dataset?
Let's say I have 3 categorical and 2 continuous attributes in a dataset. How do I build a decision tree using these 5 variables?
Edit:
For categorical variables, it is easy to say that we will split ...
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Feature Transformation on Input data
I was reading about the solution to this OTTO Kaggle challenge and the first place solution seems to use several transforms for the input data X, for example Log(X+1), sqrt( X + 3/8), etc. Is there a ...
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What does the output of model.predict function from Keras mean?
I have built a LSTM model to predict duplicate questions on the Quora official dataset. The test labels are 0 or 1. 1 indicates the question pair is duplicate. After building the model using ...
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What kinds of learning problems are suitable for Support Vector Machines?
What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines?
In other words, what is it that, when you see a learning problem, makes ...
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What's the difference between the cell and hidden state in LSTM?
LSTM cells consist of two types of states, the cell state and hidden state.
How do cell and hidden states differ, in terms of their functionality? What information do they carry?
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What is the meaning of term Variance in Machine Learning Model?
I am familiar with terms high bias and high variance and their effect on the model.
Basically your model has high variance when it is too complex and sensitive too even outliers.
But recently I was ...
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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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In SVM Algorithm, why vector w is orthogonal to the separating hyperplane?
I am a beginner on Machine Learning.
In SVM, the separating hyperplane is defined as $y = w^T x + b$.
Why we say vector $w$ orthogonal to the separating hyperplane?
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Uploading images folder from my system into Google Colab
I want to train a deep learning model on a dataset containing around 3000 images. Since the dataset is huge, I want to use Google colab since it's GPU supported. How do I upload this full image folder ...
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What statistical model should I use to analyze the likelihood that a single event influenced longitudinal data
I am trying to find a formula, method, or model to use to analyze the likelihood that a specific event influenced some longitudinal data. I am having difficultly figuring out what to search for on ...
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What are kernel initializers and what is their significance?
I was looking at code and found this:
model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
I was keen to know about ...
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Nearest neighbors search for very high dimensional data
I have a big sparse matrix of users and items they like (in the order of 1M users and 100K items, with a very low level of sparsity). I'm exploring ways in which I could perform kNN search on it. ...
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How does Gradient Descent and Backpropagation work together?
Please forgive me as I am new to this. I have attached a diagram trying to model my understanding of neural network and Back-propagation? From videos on Coursera and resources online I formed the ...
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Why do activation functions have to be monotonic?
I am currently preparing for an exam on neural networks. In several protocols from former exams I read that the activation functions of neurons (in multilayer perceptrons) have to be monotonic.
I ...
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How to add non-image features along side images as the input of CNNs
I'm training a convolutional neural network to classify images on fog conditions (3 classes). However, for each of about 150.000 images I also have four meteorological variables available that might ...
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Dimensionality and Manifold
A commonly heard sentence in unsupervised Machine learning is
High dimensional inputs typically live on or near a low dimensional
manifold
What is a dimension? What is a manifold? What is the ...
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How to generate synthetic dataset using machine learning model learnt with original dataset?
Generally, the machine learning model is built on datasets. I'd like to know if there is any way to generate synthetic dataset using such trained machine learning model preserving original dataset ...
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XGBRegressor vs. xgboost.train huge speed difference?
If I train my model using the following code:
...
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Lightgbm vs xgboost vs catboost
I've seen that in Kaggle competitions people are using lightgbms where they used to use xgboost. My question is: when would you rather use xgboost instead of lightgbm? What about catboost?
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How to perform feature engineering on unknown features?
I am participating on a kaggle competition. The dataset has around 100 features and all are unknown (in terms of what actually they represent). Basically they are just numbers.
People are performing ...
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Doc2Vec - How to label the paragraphs (gensim)
I am wondering how to label (tag) sentences / paragraphs / documents with doc2vec in gensim - from a practical standpoint.
Do you need to have each sentence / paragraph / document with its own ...
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How to choose the features for a neural network?
I know that there is no a clear answer for this question, but let's suppose that I have a huge neural network, with a lot of data and I want to add a new feature in input. The "best" way ...
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What are the pros and cons of Keras and TFLearn?
What are the pros and cons of Keras and TFlearn? When is one library preferred over the other?
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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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How to increase accuracy of classifiers?
I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and ...
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How Do I Learn Neural Networks?
I'm a freshman undergraduate student (mentioning this so you may forgive my unfamiliarity) who is currently doing research using neural networks. I've coded a three-node neural network (that works) ...
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How can I predict traffic based on previous time series data?
If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic?
I have looked into machine learning ...
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Incremental Learning with sklearn: warm_start, partial_fit(), fit()
I have built an ML model with the goal of making predictions for targets of the following week. In general, new data will come in and be processed at the end of each week and be in the same data ...
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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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Image resizing and padding for CNN
I want to train a CNN for image recognition. Images for training have not fixed size. I want the input size for the CNN to be 50x100 (height x width), for example. When I resize some small sized ...
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Tool to label images for classification
Can anyone recommend a tool to quickly label several hundred images as an input for classification?
I have ~500 microscopy images of cells. I would like to assign categories such as ...
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What does embedding mean in machine learning?
I just met a terminology called "embedding" in a paper regarding deep learning. The context is "multi-modal embedding"
My guess: embedding of something is extract some feature of sth,to form a vector....
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What does it mean to "share parameters between features and classes"
When reading this paper there is a line which says "linear classifiers do not share parameters among features and classes." What is the meaning of this statement? Does it mean that linear ...
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What is LSTM, BiLSTM and when to use them?
I am very new to Deep learning and I am particularly interested in knowing what are LSTM and BiLSTM and when to use them (major application areas). Why are LSTM and BILSTM more popular than RNN?
Can ...
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How to get predictions with predict_generator on streaming test data in Keras?
In the Keras blog on training convnets from scratch, the code shows only the network running on training and validation data. What about test data? Is the validation data the same as test data (I ...
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What is the difference between CountVectorizer token counts and TfidfTransformer with use_idf set to False?
We can use CountVectorizer to count the number of times a word occurs in a corpus:
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Can the number of epochs influence overfitting?
I am using a convolution neural network ,CNN. At a specific epoch, I only save the best CNN model weights based on improved validation accuracy over previous epochs....
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