Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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472
votes
11answers
524k views

What is the difference between test set and validation set?

I found this confusing when I use the neural network toolbox in Matlab. It divided the raw data set into three parts: training set validation set test set I notice in many training or learning ...
450
votes
20answers
153k views

The Two Cultures: statistics vs. machine learning?

Last year, I read a blog post from Brendan O'Connor entitled "Statistics vs. Machine Learning, fight!" that discussed some of the differences between the two fields. Andrew Gelman responded favorably ...
392
votes
5answers
130k views

How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just ...
270
votes
8answers
88k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
258
votes
8answers
186k views

Bagging, boosting and stacking in machine learning

What's the similarities and differences between these 3 methods: Bagging, Boosting, Stacking? Which is the best one? And why? Can you give me an example for each?
226
votes
3answers
20k views

How to know that your machine learning problem is hopeless?

Imagine a standard machine-learning scenario: You are confronted with a large multivariate dataset and you have a pretty blurry understanding of it. What you need to do is to make predictions ...
217
votes
13answers
198k views

What is the difference between data mining, statistics, machine learning and AI?

What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different ...
196
votes
4answers
177k views

What does the hidden layer in a neural network compute?

I'm sure many people will respond with links to 'let me google that for you', so I want to say that I've tried to figure this out so please forgive my lack of understanding here, but I cannot figure ...
189
votes
4answers
77k views

ROC vs precision-and-recall curves

I understand the formal differences between them, what I want to know is when it is more relevant to use one vs. the other. Do they always provide complementary insight about the performance of a ...
177
votes
9answers
37k views

Why the sudden fascination with tensors?

I've noticed lately that a lot of people are developing tensor equivalents of many methods (tensor factorization, tensor kernels, tensors for topic modeling, etc) I'm wondering, why is the world ...
165
votes
3answers
77k views

Generative vs. discriminative

I know that generative means "based on $P(x,y)$" and discriminative means "based on $P(y|x)$," but I'm confused on several points: Wikipedia (+ many other hits on the web) classify things like SVMs ...
163
votes
9answers
69k views

Why is Newton's method not widely used in machine learning?

This is something that has been bugging me for a while, and I couldn't find any satisfactory answers online, so here goes: After reviewing a set of lectures on convex optimization, Newton's method ...
160
votes
7answers
153k views

What are the advantages of ReLU over sigmoid function in deep neural networks?

The state of the art of non-linearity is to use rectified linear units (ReLU) instead of sigmoid function in deep neural network. What are the advantages? I know that training a network when ReLU is ...
158
votes
5answers
47k views

Training on the full dataset after cross-validation?

TL:DR: Is it ever a good idea to train an ML model on all the data available before shipping it to production? Put another way, is it ever ok to train on all data available and not check if the model ...
154
votes
4answers
109k views

Choice of K in K-fold cross-validation

I've been using the $K$-fold cross-validation a few times now to evaluate performance of some learning algorithms, but I've always been puzzled as to how I should choose the value of $K$. I've often ...
153
votes
2answers
163k views

A list of cost functions used in neural networks, alongside applications

What are common cost functions used in evaluating the performance of neural networks? Details (feel free to skip the rest of this question, my intent here is simply to provide clarification on ...
152
votes
7answers
242k views

What is the influence of C in SVMs with linear kernel?

I am currently using an SVM with a linear kernel to classify my data. There is no error on the training set. I tried several values for the parameter $C$ ($10^{-5}, \dots, 10^2$). This did not ...
136
votes
9answers
53k views

Obtaining knowledge from a random forest

Random forests are considered to be black boxes, but recently I was thinking what knowledge can be obtained from a random forest? The most obvious thing is the importance of the variables, in the ...
135
votes
9answers
54k views

Why is accuracy not the best measure for assessing classification models?

This is a general question that was asked indirectly multiple times in here, but it lacks a single authoritative answer. It would be great to have a detailed answer to this for the reference. ...
131
votes
2answers
138k views

Gradient Boosting Tree vs Random Forest

Gradient tree boosting as proposed by Friedman uses decision trees as base learners. I'm wondering if we should make the base decision tree as complex as possible (fully grown) or simpler? Is there ...
127
votes
4answers
77k views

How to intuitively explain what a kernel is?

Many machine learning classifiers (e.g. support vector machines) allow one to specify a kernel. What would be an intuitive way of explaining what a kernel is? One aspect I have been thinking of is ...
115
votes
5answers
64k views

How does a Support Vector Machine (SVM) work?

How does a Support Vector Machine (SVM) work, and what differentiates it from other linear classifiers, such as the Linear Perceptron, Linear Discriminant Analysis, or Logistic Regression? * (* I'm ...
109
votes
5answers
19k views

What skills are required to perform large scale statistical analyses?

Many statistical jobs ask for experience with large scale data. What are the sorts of statistical and computational skills that would be need for working with large data sets. For example, how about ...
109
votes
8answers
14k views

Detecting a given face in a database of facial images

I'm working on a little project involving the faces of twitter users via their profile pictures. A problem I've encountered is that after I filter out all but the images that are clear portrait ...
108
votes
8answers
85k views

Objective function, cost function, loss function: are they the same thing?

In machine learning, people talk about objective function, cost function, loss function. Are they just different names of the same thing? When to use them? If they are not always refer to the same ...
104
votes
7answers
86k views

What is the difference between off-policy and on-policy learning?

Artificial intelligence website defines off-policy and on-policy learning as follows: "An off-policy learner learns the value of the optimal policy independently of the agent's actions. Q-learning ...
103
votes
6answers
38k views

Is it possible to train a neural network without backpropagation?

Many neural network books and tutorials spend a lot of time on the backpropagation algorithm, which is essentially a tool to compute the gradient. Let's assume we are building a model with ~10K ...
102
votes
1answer
59k views

Conditional inference trees vs traditional decision trees

Can anyone explain the primary differences between conditional inference trees (ctree from party package in R) compared to the ...
101
votes
11answers
41k views

When should linear regression be called “machine learning”?

In a recent colloquium, the speaker's abstract claimed they were using machine learning. During the talk, the only thing related to machine learning was that they perform linear regression on their ...
100
votes
4answers
75k views

How to select kernel for SVM?

When using SVM, we need to select a kernel. I wonder how to select a kernel. Any criteria on kernel selection?
100
votes
2answers
76k views

What is an embedding layer in a neural network?

In many neural network libraries, there are 'embedding layers', like in Keras or Lasagne. I am not sure I understand its function, despite reading the documentation. For example, in the Keras ...
98
votes
11answers
33k views

Explain “Curse of dimensionality” to a child

I heard many times about curse of dimensionality, but somehow I'm still unable to grasp the idea, it's all foggy. Can anyone explain this in the most intuitive way, as you would explain it to a child,...
97
votes
3answers
150k views

How do you calculate precision and recall for multiclass classification using confusion matrix?

I wonder how to compute precision and recall using a confusion matrix for a multi-class classification problem. Specifically, an observation can only be assigned to its most probable class / label. I ...
95
votes
7answers
45k views

Bias and variance in leave-one-out vs K-fold cross validation

How do different cross-validation methods compare in terms of model variance and bias? My question is partly motivated by this thread: Optimal number of folds in $K$-fold cross-validation: is leave-...
95
votes
5answers
103k views

What are the main differences between K-means and K-nearest neighbours?

I know that k-means is unsupervised and is used for clustering etc and that k-NN is supervised. But I wanted to know concrete differences between the two?
94
votes
4answers
46k views

Why are neural networks becoming deeper, but not wider?

In recent years, convolutional neural networks (or perhaps deep neural networks in general) have become deeper and deeper, with state-of-the-art networks going from 7 layers (AlexNet) to 1000 layers (...
94
votes
5answers
83k views

What does a “closed-form solution” mean?

I have come across the term "closed-form solution" quite often. What does a closed-form solution mean? How does one determine if a close-form solution exists for a given problem? Searching online, I ...
91
votes
2answers
106k views

tanh activation function vs sigmoid activation function

The tanh activation function is: $$tanh \left( x \right) = 2 \cdot \sigma \left( 2 x \right) - 1$$ Where $\sigma(x)$, the sigmoid function, is defined as: $$\sigma(x) = \frac{e^x}{1 + e^x}$$. ...
88
votes
7answers
48k views

Why use gradient descent for linear regression, when a closed-form math solution is available?

I am taking the Machine Learning courses online and learnt about Gradient Descent for calculating the optimal values in the hypothesis. h(x) = B0 + B1X why we ...
84
votes
3answers
24k views

Why isn't Logistic Regression called Logistic Classification?

Since Logistic Regression is a statistical classification model dealing with categorical dependent variables, why isn't it called Logistic Classification? Shouldn't the "Regression" name be reserved ...
82
votes
7answers
29k views

Euclidean distance is usually not good for sparse data (and more general case)?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
82
votes
2answers
50k views

Solving for regression parameters in closed-form vs gradient descent

In Andrew Ng's machine learning course, he introduces linear regression and logistic regression, and shows how to fit the model parameters using gradient descent and Newton's method. I know gradient ...
81
votes
6answers
34k views

Feature selection for “final” model when performing cross-validation in machine learning

I am getting a bit confused about feature selection and machine learning and I was wondering if you could help me out. I have a microarray dataset that is classified into two groups and has 1000s of ...
79
votes
10answers
119k views

Validation Error less than training error?

I found two questions here and here about this issue but there is no obvious answer or explanation yet.I enforce the same problem where the validation error is less than training error in my ...
79
votes
4answers
119k views

Softmax vs Sigmoid function in Logistic classifier?

What decides the choice of function ( Softmax vs Sigmoid ) in a Logistic classifier ? Suppose there are 4 output classes . Each of the above function gives the probabilities of each class being the ...
79
votes
1answer
7k views

Help me understand Support Vector Machines

I understand the basics of what a Support Vector Machines' aim is in terms of classifying an input set into several different classes, but what I don't understand is some of the nitty-gritty details. ...
78
votes
2answers
21k views

Why is ridge regression called “ridge”, why is it needed, and what happens when $\lambda$ goes to infinity?

Ridge regression coefficient estimate $\hat{\beta}^R$ are the values that minimize the $$ \text{RSS} + \lambda \sum_{j=1}^p\beta_j^2. $$ My questions are: If $\lambda = 0$, then we see that the ...
78
votes
8answers
98k views

How to compute precision/recall for multiclass-multilabel classification?

I'm wondering how to calculate precision and recall measures for multiclass multilabel classification, i.e. classification where there are more than two labels, and where each instance can have ...
77
votes
3answers
60k views

Best way to present a random forest in a publication?

I am using the random forest algorithm as a robust classifier of two groups in a microarray study with 1000s of features. What is the best way to present the random forest so that there is enough ...

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