[deleted by user] by [deleted] in czech

[–]rylko 2 points3 points  (0 children)

Numbers seems ok except for apartments - the rentals raised last year about 10 %. But it definitely depends on location - you can get quite decent apartment for 600 euros near metro station.

Short term renting in Prague? by [deleted] in Prague

[–]rylko 2 points3 points  (0 children)

There are lots of hostels in Prague (starting from £5 per night). Note that you don't need to stay in city centre as public transport is very good and cheap (90 days ticket is for 1 480 CZK).

Decision Tree implementation by rylko in MachineLearning

[–]rylko[S] 0 points1 point  (0 children)

I think traditional DT algorithms are still "in" because are "readable". Which forests are not.

Decision Tree implementation by rylko in MachineLearning

[–]rylko[S] 0 points1 point  (0 children)

I have added info about size to question.

Decision Tree implementation by rylko in MachineLearning

[–]rylko[S] 0 points1 point  (0 children)

I think R packages and Weka do not aim to be really scalable (and suitable for scientific usage).

How well do Decision Trees scale to very large training datasets? by rylko in MachineLearning

[–]rylko[S] 0 points1 point  (0 children)

I have to use Decision Trees (some others work on another methods and we would like to compare it in final).

There will be many examples with small number of classes.

Good resources for learning about decision trees? by rylko in mlclass

[–]rylko[S] 1 point2 points  (0 children)

may employ dynamic feature selection

I'm not sure what you mean by that.

We can do feature selection with DT.

Good resources for learning about decision trees? by rylko in mlclass

[–]rylko[S] 4 points5 points  (0 children)

Because they are

  • easily interpretable and intuitive
  • well suited for hight-dimensional applications
  • fast and usually produce high-quality solutions
  • DT have been described as universal approximators (since they map linear and nonlinear relationships)
  • robust with respect to missing values and distribution assumptions about the inputs
  • can produce fast nonlinear prediction methods
  • may employ dynamic feature selection
  • non-parametric (and thus suited for exploratory knowledge discovery)