I have some data about maximum likelihood estimates. We have fields of estimate, standard error, pr>chiSq and Walk Square. I have no idea what the Walk Square is referring to - it feels like some measure of the distance, but it is not a term I have encountered before. any ideas?
The only success I've seen or had with Neural Nets, is taking whatever input, and outputting Boolean results, yes/no, in the form of a range between in the case in question $0.5$ and $1.0$, with $0< Y_n\le0.75 = $No and $0.75<Y_n\le1.0 = $Yes. But what if I want a usable value? Like a true blue regression of a complex equation? I see examples of this but I don't see proof. But for sake of argument let's say that I have …
The given below code snippet is from the assignment of online course IBM ML with Python. Here's the assignment. The used variable names :mean_acc and std_acc are ambiguous for me. So, I am thinking from the point of Inferential Statistics but it conflicts. Ks = 10 mean_acc = np.zeros((Ks-1)) std_acc = np.zeros((Ks-1)) for n in range(1,Ks): #Train Model and Predict neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train) yhat=neigh.predict(X_test) mean_acc[n-1] = metrics.accuracy_score(y_test, yhat) std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0]) Visualisation plt.plot(range(1,Ks),mean_acc,'g') plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 …
English is not my mother tongue; please excuse any errors on my part. I've recently faced a problem for which I haven't found any solutions after investing a lot of time. Here is the summarized problem statement: Imagine our dataset being the history of matches of a football team, and each point in our dataset is either zero (lost) or one (won). Here we define four metrics that we want to estimate; winning ratio (# of wins / # total …
I am trying to estimate how much a user liked a video using how much of the video they watched. Let's say, on the scale of 1 to 10, 1 means that the user didn't like it at all, and 10 means they enjoyed it a lot. For instance, if a user watched 8 minutes from a 10-minute video, it means the score of 8. If they watch 18 minutes of a 20-minute video, it means the score of 9. …
I trained a model that predicts customer's income given the features: age, declared income number of oustanding instalment, overdue total amount active credit limit, total credit limit total amount The output is a prediction: lower-upper bound for a customer: e.g. [8756-9230] Metrics used: NIRDM - not in range distance mean - how far the value is from the closest bound (on average) for values out of range(similar to true negative) in-interval - percent of tested values that actually happen to …
Test statistic and sample statistic are used to check statistical significance. How do I understand the procedure in background of reaching a conclusion about an effect-size estimate.
I was reading this article https://www.di.ens.fr/~aspremon/PDF/CovSelSIMAX.pdf, whose goal is to estimate the covariance matrix from a the sample covariance matrix drawn from a distribution $X$. ' Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables.' The likelihood problem is only for the case where …
I want to estimate my daily water consumption. I have taken pictures of the water meters (total m3 used since last reset) every now and then, but without any regularity. There can be a difference of a few days to several weeks between samples. What would be the best way to estimate this? I have thought of the following approaches: Create a double-entry table with the sample dates in the column and in the row headers. Each cell is the …