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Results tagged with machine-learning
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user 24515
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.
3
votes
What is the difference between Econometrics and Machine Learning?
Some notes in addition to @JustMe:
First, there is a lot of arrogance on both sides of Econometrics and Machine Learning. Discussing which of the two may be a sub-discipline of which is futile. In f …
8
votes
1
answer
1k
views
Is Monte Carlo cross-validation procedure valid?
I thought K-fold cross-validation consists of the following steps.
Split data randomly into $K$ chunks.
Fit on $K-1$ chunks.
Predict on remaining chunk. Keep predictions.
Repeat 2-3 for all remanini …
- 6,704
5
votes
1
answer
904
views
What are the primary advantages of using Kernels in predicting continuous outcomes?
Consider the linear model: $$y = X \theta + \epsilon $$ with $X$ inputs or features of inputs and $\theta$ a vector of parameters (and $\epsilon$ the error) with regularized error function $$ J(w)= \f …
- 6,704
0
votes
0
answers
16
views
Which machine learning algorithm may help to explore the importance of contributions of vari...
I have data in the form of a $n \times t$ matrix $X$ where $n$ is a number of variables and $t$ a (large) number of time points. At any given time point the elements of the matrix can be expressed as …
- 6,704
3
votes
Accepted
Using partial measurements of output variable in modeling
Fun question. The key problem as noted by @MartijnWeterings is that the number of trees at phase 2 is only a partial measurement of the total number of trees. If we knew the total number of trees, how …
- 6,704
2
votes
Is the Gaussian Kernel still a valid Kernel when taking the negative of the inner function?
After some more thinking I will make an attempt to answer my own question. From Bishop's Pattern Recognition and Machine Learning, p. 296, I take rules for building new Kernels from valid Kernels. Let …
- 6,704
4
votes
Accepted
Machine learning to estimate p(y>N | X) where N is a duration
You want to estimate the probability $P(y>Y|X)$ where $Y$ is a survival time variable, and $y$ is a particular time value (that you called $N$).This problem can be approached from two angles.
First o …
- 6,704
12
votes
Accepted
Methods to work around the problem of missing data in machine learning
The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well worki …
- 6,704
3
votes
1
answer
78
views
How to choose the model hyperparameter after cross-validation when the model fit indices are...
I cross-validated a model using the classification accuracy using leave-one-out-cross-validation (proportion of correctly classified cases). Below is a matrix of accuracies typical to what I see. The …
- 6,704
11
votes
3
answers
5k
views
Is the Gaussian Kernel still a valid Kernel when taking the negative of the inner function?
In support vector machines (SVMs) and other Kernel based methods, like Gaussian processes, the Kernel replaces the inner product of two feature vectors $k(x_n,x_m)=x_n^Tx_m$. The Gaussian kernel
$$k( …
- 6,704
4
votes
0
answers
40
views
Which ML algorithms can be used to optimize a weighted quadratic loss function?
I want to solve the following optimization problem:
$$ L = n^{-1} \sum^n_{i=1} w_i ( y_i - \tau(x_i))^2 $$
where $w_i \in \mathbb{R}^+$ weights, $y_i \in \mathbb{R}$ outcome data, $x_i$ features/cova …
- 6,704
3
votes
0
answers
74
views
Does anybody know this measure of model fit / prediction error?
Let $y_i$ be the true value and $\hat{y}_i$ a prediction from a model. Then, for example $$B=n^{-1}\sum_{i=1}^n \hat{y}_i - y_i$$ is the prediction bias and $$MSE=n^{-1}\sum_{i=1}^n (\hat{y}_i - y_i)^ …
- 6,704
37
votes
4
answers
18k
views
LASSO with interaction terms - is it okay if main effects are shrunk to zero?
LASSO regression shrinks coefficients towards zero, thus providing effectively model selection. I believe that in my data there are meaningful interactions between nominal and continuous covariates. N …
- 6,704
1
vote
0
answers
66
views
What are useful machine learning technqiues for prediction from this time series data?
My question is which machine learning techniques can be used to make predictions based on time series data.
My data is of the form of a series of discrete, binary measurements $y_t \in \{0,1\}$ for $ …
- 6,704
2
votes
Accepted
Examples where the evaluation of the posterior distribution $p(Z|X)$ of the latent variables...
Any method involving a latent variable can be represented in the terms you mentioned and the EM algorithm is often used as a part of the estimation or prediction procedure for $Z$, such as
Mixture m …
- 6,704