Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with machine-learning
Search options not deleted
user 169343
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.
0
votes
Suitable machine learning methods for failure detection in a technical system
Anomaly detection usually works by assuming some "normal" (in the sense of "correct", "non-anomalous") distribution of the measured values. This assumed distribution is usually "normal" (now meaning " …
- 9,678
1
vote
Is there a "canonical" probabilistic version of the step function?
Depending on the underlying continuous probability distribution(s), different "soft" switch functions may appear.
$ f(x) = \frac{1}{2} + \frac{1}{2} \tanh (rx)$, also known as the logistic function, a …
- 9,678
11
votes
Why do parameters go untested in Machine Learning?
tl;dr: Because it's 1) not possible and 2) not necessary.
Long answer: Your question is posed from a statistical perspective, where testing the parameters is a standard thing to do. This makes it soun …
- 9,678
4
votes
Accepted
How to draw the single perceptron decision boundary when weights and bias are 0?
TL;DR: Your decision boundary is the whole $(x_1, x_2)$ plane.
In detail: The function
$$
z = w_1 x_1 + w_2 x_2 - b
$$
is a plane in the 3D space, spanned by the axes $(x_1, x_2, z)$. Where $z = 0$, t …
- 9,678
0
votes
Coefficient for linear and non-linear regression
"Linearity" is not an issue here. You can most likely interpret your regression as a linear regression over non-linearly transformed variable. What matters is your loss function. If you're minimising …
- 9,678
0
votes
Is machine learning all about hyperparameter tuning?
It depends on your definition of "hyperparameters": Is the model itself a hyperparameter? The loss function? The choice of input features? The preprocessing method(s)? Etc.
If yes, then your view can …
- 9,678
0
votes
Valid references on origins of Machine Learning, Statistical Learning and Data Mining
In addition to Mike Hunter's excellent answer (+1), I'd like to point out the envisioned application fields of statistics, contrasted to Machine Learning.
Statistics was historically developed as a to …
- 9,678
1
vote
Support vector classifier / soft margin classifier
No. The two are independent of each other.
The kernel $K(x, z)$ is the generalisation of the scalar (dot) product, $x \cdot z$. We often choose $K(x, z)$ to be a non-linear function (polynomial, RBF, …
- 9,678
3
votes
What is the point of test set in ML?
The test set is used to assess the performance of the trained ML system. In the process of training, both the training and the validation (dev) set contribute to the parameters of the system: You opti …
- 9,678
3
votes
What is the intuition behind what Neural Networks do to data that is 1 dimensional?
Jan has actually captured the essence of the answer in his comment, but I'll try to make it more explicit.
This is not specific to parameter sharing; it is what neural networks typically do. What th …
- 9,678
2
votes
0
answers
71
views
$F_1$ score generalised to probabilities: Why squares in the denominator?
I recently stumbled over a generalisation of $F_1$ score to cases where the model predicts probabilities:
$$
F_1 = 2 \frac{\sum y_i \hat{p}_i}{\sum y_i^2 + \sum \hat{p}_i^2}
$$
where $y_i \in \{ 0, 1 …
- 9,678
2
votes
Accepted
how to calculate b in SVM if we have the optimum solution
For any support vector $\textbf{x}^{(i)}$, the following holds:
$$
\textbf{w}^T \cdot \textbf{x}^{(i)} + b = y^{(i)}.
$$
This is basically the definition of "support vector": It lies on the margin, ei …
- 9,678
1
vote
What exactly is the bias when using training / validation / testing data?
Bias refers to one component of the error of your model. The other component is variance, and there is a trade-off between the two.
Bias comes essentially from the assumptions you (or your model) mak …
- 9,678
1
vote
How to cluster multiple datasets
If I understand correctly, you can construct a new "dataset", in which each node (or original dataset) is one observation (point). You need to devise a metric for calculating the "distance" between th …
- 9,678
0
votes
Which loss funtion should i use in Regression problems?
It depends on the probability distributions of the errors, unexplained differences between your model and the observed data. MSE is appropriate when you expect the errors to be normally distributed. T …
- 9,678