Each circle around your point $\beta$ is actually an isoline in the 3rd dimension, i.e. upwards, and every points on such a line have the same value for the loss function. You could draw infinitely ...

An exact expression for the ratio of two correlated normal random variables was given by Hinkley in 1969. The notation is taken from the stackexchange post For $Z = \frac{X}{Y}$ with $$ \begin{...

You could use PDPBox which is compatible with all Sklearn algorithms https://github.com/SauceCat/PDPbox

A few ideas (out of many possible approaches) Use PCA to reduce the dimensionality (i.e. keep top 10 PCA dimensions) Use some form of feature selection to remove the non-informative features Use a ...

My question is, how do I extract the 'equation' out from the model It all depends on the model you are using. Some models do not have a straighrforward formula you can extract, and are often called ...

Clarifying what is meant by $\alpha$ and Elastic Net parameters Different terminology and parameters are used by different packages, but the meaning is generally the same: The R package Glmnet uses ...

I presume you are interested in the intrinsic quality of an algorithm. This is a non trivial question and the topic of active research. Bounds on the bias and variance of an algorithm can be proven ...

The inclination of some collaborators is to go with the complete case type analysis, where only subjects with full data are used, but this makes me slightly nervous, as I feel like those missing data ...

1) Assuming you have properly pre-processed your data then I would consider the absolute value of the weight. Negative value just means that it has a negative impact on the outcome, but a large ...

Simulating the difference of means of Gamma populations Comparing the t-test and the Mann Whitney test Summary of results When the variance of the two populations is the same, the Mann Whitney test ...

Comments from Daniel J. McDonald Assistant professor at Indiana University Bloomington, author of the two papers mentioned in the original response from Xavier Bourret Sicotte. Your explanation ...

UPDATE See this second post for McDonald's feedback on my answer where the notion of risk consistency is related to stability. 1) Uniqueness vs Stability Your question is difficult to answer ...

2D graph visualization T-SNE is obviously the first thing to come to mind, but you could perhaps push the exercise further by applying 2D graph visualization. Here is a good example from Sklearn ...

A first answer Providing a definitive answer wil be difficult without knowing more about your data, understanding the models and chosen hyperparameters, and performing various checks on the results. ...

This model is not a regressor. It is a classifier, with a variable reward/penalty moreover. So I cannot calculate the MSE of validation folds, there is no MSE here. How can I apply k-fold validation ...

I would argue that your claims, and diagram, are incorrect generalizations and can be misleading. Definitions and terminology Based on the terminology and definitions from Cross Validation and ...

Recall that the Logistic regression model is a non linear transformation of $\beta^Tx$ Probability of $(Y = 1)$: $p = \frac{e^{\alpha + \beta_1x_1 + \beta_2 x_2}}{1 + e^{ \alpha + \beta_1x_1 + \...

I suppose the answer depends on what you are trying to accomplish: Predicting future values Performing statistical inference Other ? My answer is that you can use any of the many regression models ...

LASSO is unstable for feature selection The combination of LASSO and Cross Validation isn't always unstable for feature selection. Factors leading to instability are: More features than data points: ...

Background Consider the following definitions: A training set $D = {z_1,...,z_n}$ with $Z_i \in Z$ independently sampled from an unknown distribution $P$. An algorithm $A$ which maps a data set to ...

A comparison of confidence intervals methods on an example from ISL The book "Introduction to Statistical Learning" by Tibshirani, James, Hastie provides an example on page 267 of confidence ...

how does lasso method find which features are redundant to shrink their coefficients to zero? The Lasso method on its own does not find which features to shrink. Instead, it is a combination of Lasso ...

Clarifying your approach the 0 lasso coefs changed What value of $\lambda$ (the Lasso parameter) did you use and how did you determine it ? Your approach seems confusing: Have you performed k-...

If your data meets the required assumptions you can perform linear regression and use the model to predict points - see here for an example Testing Hypothesis with Time series and Location Data If the ...

While Ert's and Ben's answers are excellent, they rely on the assumption that you understand how to perform statistical modelling on time series, which is not a trivial topic. Entire books are written ...

Is there anyway to implement Locally Weighted Linear Regression without these problems? (preferably in Python) Yes, you can use Alexandre Gramfort's implementation - available on his Github page. (...

I am doing repeated cross-validation/holdout, because the results vary a LOT depending on the splits. To me it seems that your model (which you don't talk about but probably should) is unstable. ...

There has been much debate, confusion and contradiction on this topic, both on stats.stackexchange and in scientific literature. A useful paper is the 2004 study by Bengio & Grandvalet which ...

Why find the weak learner by the formula in the blue box? You define a class of weak learners as part of your boosting algorithm. For example you can perform boosting on decision trees, on SVM, ...

When using k fold cross validation method, to plot the learning curve, would training error be the misclassification error on DataTrain and cross-validation error be the misclassification error using ...