Rattle | new imperative programming language designed to be | Interpreter library

Have you tried your own roc_auc scoring rule? It seems like you are passing labels instead of probabilities (you originally need) for roc_auc.

problem described in here: Different result roc_auc_score and plot_roc_curve

Solutions for own scorers: Grid-Search finding Parameters for AUC

Update2

Sorry, saw today that my introduction text from the notebook was missing lol

When calculating roc_auc_score you have the option (it doesnt matter, if it is with or without gridsearch, with or without pipeline) that you can pass it labels like (0/1) or probabilities like (0.995, 0.6655). The first should be easy available if you just convert your probas to labels. However that would result in a (straight reversed L) output plot. That looks sometimes ugly. the other option is to use predicted probabilites to pass them them to the roc_auc_score. That would result in a (staircase reversed L) output plot, which looks much better. So what you first should test is, can you get a roc auc score with labels, with and without grid, if that is the case. You should then try to get probabilities. And there, I believe, you have to write your own scoring method, as the roc-auc_score in grid only serves labels, that would result in high roc_auc scores. I wrote something for you, so you can see the label approach:

Not sure if I understood correctly but I think this may work:

If you want to sum the Price of the current group, you need to use the key to select the group:

The server process must be listening on the special "all interfaces" address 0.0.0.0. Many development-type servers by default listen on "localhost only" 127.0.0.1, but in Docker each container has its own private notion of localhost. If you use tmux or docker exec to run multiple processes inside a container, they have the same localhost and can connect to each other, but if the client and server are running in different containers, the request doesn't arrive on the server's localhost interface, and if the server is listening on "localhost only" it won't receive it.

Your setup is otherwise correct, with only the docker-compose.yml you include in the question. Some other common problems:

• You must connect to the port the server process is listening on inside the container. If you remap it externally with ports:, that's ignored, and you'd connect to the second ports: number. Correspondingly, ports: aren't required. (expose: also isn't required and doesn't do anything at all.)
• The client may need to wait for the server to start up. If the client depends_on: [flask] the host name will usually resolve (unless the server dies immediately) but if it takes a while to start up you will still get "connection refused" errors. See Docker Compose wait for container X before starting Y.
• Neither container may use network_mode: host. This disables Docker's networking features entirely.
• If you manually declare networks:, both containers need to be on the same network. You do not need to explicitly create a network for inter-container communication to work: Compose provides a default network for you, which is used if nothing else is declared.
• Use the Compose service names as host names. You don't need to explicitly specify container_name: or links:.

The problem seems to be that Rattle is seeing the df object as the stats package call to the df function for the F distribution. The same error occurs when naming the object dt in the code above -- Rattle fails because (I think) it considers dt the T distribution function. Changing the object name to dt1 or df1 works just fine in Rattle.

This wasn't noticeable when I tried class(df) or typeof(df). While it's dangerous to name objects with existing function/object names, df for data frame and dt for data table are commonly seen.

Issue 1: Use match parent in both height and width in item.xml

Issue 2: layout_height="wrap_content" can't be used in GridView in fact it can't be used in subclasses of AdapterView (e.g. ListView and GridView). You'll have to calculate each item's height dynamically and then set the gridview height accordingly. Instead of doing this much work, just use a recyclerview with a grid layout and set its height as wrap_content

and also disable nested scrolling in this recyclerview

This is because of Series.mean() doesn't count the NaN values in the dataFrame. if you want to include NaN values you can use Series.mean(skipna=False).

HAPPY_CODING...

I think the best way to do this is to create a little wrapper function, maybe called fit_titanic():

Use newdata = within predict function for both training and testing data like