Thank you for an excellent presentation. JASP has become extremely impressive. The team and community behind Jamovi and JASP are making a statistically significant software of a gem.
Can you specify which subjects the model should use? Let's say I trained my model and collected approximately 20 subjects, and now I want to see how well the model predicts disease for this sample whilst being trained on the prior data set.
I am moving from SPSS to JASP, and will be using this for my quantitative analysis callses to my MA sociology students, found your presentation really useful.
Sir i have watched your video and totally agreed with you that there is no rule of thumb.. According to my littlr knowledge what i usually intrepret as a statistician is i compare the MAE OR MSE sets of different input combination and look minimal value.
Really good presentation, thank you for that! My question is, let's say we completed the training and testing part and saw that we have a model that has high accuracy. How can we deploy it to make further predictions with new inputs?
@@VahidAryadoust Sorry for unclear question. For example, we use dataset A generate a machine learning model B in JASP. Is it possible to use model B to do prediction in other dataset while we do not have correct answer.
You should feed the new dataset as your left-out (testing) data. Simply create a new variable in the data (call it test-train). Test = 1 and train =2. Replace the test data with your new data and run the analysis again.
If I may, I have a question. Do continious predictors need to be on similar standardized or normalized scale in order to compare their relative influence on the model?
@@VahidAryadoust Thank You for the answer and what about bi- and multi-nomial qualitative data? If I use variables measurer on different types of scales. You would also recommend unification? So for example min-max conversion of all variables?
Thank you for an excellent presentation. JASP has become extremely impressive. The team and community behind Jamovi and JASP are making a statistically significant software of a gem.
Can you specify which subjects the model should use? Let's say I trained my model and collected approximately 20 subjects, and now I want to see how well the model predicts disease for this sample whilst being trained on the prior data set.
I am moving from SPSS to JASP, and will be using this for my quantitative analysis callses to my MA sociology students, found your presentation really useful.
Really amazing presentation and clear interpretation! Thank you. is there any chance of sharing the ppt slides you employed in the presentation?
Sir i have watched your video and totally agreed with you that there is no rule of thumb.. According to my littlr knowledge what i usually intrepret as a statistician is i compare the MAE OR MSE sets of different input combination and look minimal value.
How can we use prediction algorithm for boosting regression in latest JASP version
Can you upload a video on random forest ML technique on JASP platform
Quite brilliant!
Once you have trained a model. How can you test it on new test data?
Really good presentation, thank you for that! My question is, let's say we completed the training and testing part and saw that we have a model that has high accuracy. How can we deploy it to make further predictions with new inputs?
Models cannot be deployed from JASP, as far as I know. It is an area for improvement, which you can bring up with the software developer.
Spettacolare
Really useful, but how can we apply the model into new data prediction even we do not have the correct answer. thanks your time.
Sorry could you rephrase the question? I have difficulty understanding it.
@@VahidAryadoust Sorry for unclear question. For example, we use dataset A generate a machine learning model B in JASP. Is it possible to use model B to do prediction in other dataset while we do not have correct answer.
You should feed the new dataset as your left-out (testing) data. Simply create a new variable in the data (call it test-train). Test = 1 and train =2. Replace the test data with your new data and run the analysis again.
I did not understand. can u please share a short clip. It will be very much helpful for me. Thanks
Brilliant
If I may, I have a question. Do continious predictors need to be on similar standardized or normalized scale in order to compare their relative influence on the model?
I suggest you standardize the variables if they are not on the same scale. For example, standardize them on mean = 0 and SD = 1, or other ways.
@@VahidAryadoust Thank You for the answer and what about bi- and multi-nomial qualitative data? If I use variables measurer on different types of scales. You would also recommend unification? So for example min-max conversion of all variables?