Hello!
After running a model for a simple linear regression model in pymc3, I'm trying to do a plot.
As you can see both plots show the "posterior predictive mean score", but I was wondering whether can I plot this distribution around the "posterior predictive mean score" (not around "observed score"), in arviz, similar to what is shown in the first figure?
Both figures were made using the same data:
Score <- c(78.93,58.2,67.47,37.47,45.65,32.92,29.97)
Drugs <- c(1.17,2.97,3.26,4.69,5.83,6,6.41)
EDIT:
Bayesplot with bounds:
Related
How can anchor box and NMS be applied to a test set when the Ground Truth is unknown?
Without the Ground Truth, the IOU of the anchor box is unknown, so are all the anchor boxes used to predict the box location?
Then, why are they used in training the first place.
1 - You don't need GT in the validation or test phase except measuring the performance of model. Because the model have already learned the regression coefficients in the training phase. These coefficients are used to fit the default anchor boxes to predictions.
2 - NMS eliminates the region proposals without using GT. It aims to select best proposal over the other proposals while comparing them against each other.
3 - If you don't know the GT you can't learn the classes and coordinates of objects during the training phase. So you need compare your GT with predictions.
You can check out this great article to have comprehensive understanding of R-CNN family.
I would like to know if anyone has had success labeling images with a continuous variables in the AutoML Vision platform.
Specifically, I would like to predict the height of a sand castle from a birds eye view photograph. I would train the model on birds eye view photographs of sandcastles labeled with the height in inches. I have 10,000 images in my data set. The range of heights in my data set is 1cm to 110cm, so the variable is continuous but not infinite.
Is this achievable through Google AutoML Vision?
Thank you!
As for now, there is no such feature on AutoML Vision that allows you to annotate images using continuous variables. As far as I understand, the labels are treated individually and the model will require "1000 training images per label. The minimum per label is 10, or 50 for advanced models".
A feasible approach with this would be to use discrete heights or ranges (1cm-20cm, 21cm-50cm, etc).
I'm using scatter_matrix for correlation visualization and calculating correlation values using corr(). Is it possible to have the scatter_matrix visualization draw the regression line in the scatter plots?
I think this is a misleading question/thought process.
If you think of data in strictly 2 dimension then a regression line on a scatter plot makes sense. But let's say you have 5 dimensions of data you are plotting in your scatter matrix. In this case the regression for each pair of dimensions is not an accurate representation of the global regression.
I would be wary presenting that to anyone as I can easily see where it could create confusion.
That being said if you don't care about a regression across all of your dimensions then you could write your own function to do this. A quick walk through of steps may be:
1. Identify number of dimensions N
2. Create figure
3. Double for loop on N, first will walk down rows, second will walk across rows
4. At each point add subplot, calculate regression (if not kde/hist position), plot scatter cloud and regression line or kde/hist
I have this scatter plot:
It's month-number. If I convert month to a number (1-12), I can calculate regression line like this:
Is there anyway to keep the month, not having to convert to number and still run linear regression?
Thanks!
Try leaving your scatter plot with the numbers for the x-axis, but customizing your tick labels to show months.
Add this to your options object.
hAxis:{ticks:[{v:1,f:'Jan'},{v:2,f:'Feb'},{v:3,f:'Mar'},{v:4,f:'Apr'},
{v:5,f:'May'},{v:6,f:'Jun'},{v:7,f:'Jul'},{v:8,f:'Aug'},
{v:9,f:'Sep'},{v:10,f:'Oct'},{v:11,f:'Nov'},{v:12,f:'Dec'}]}
I'm using a LMM in SAS and, I would like to get an estimation (and a p-value) of a linear combination of some of the regression coefficients.
Say that the model is:
b0+b1Time+b2X1+b3X2+b4(Time*X1)
and say that, I want to get an estimate and a p-value for the b1+b4.
What should I do?