"One of the primary reasons we evaluate machine learning models is so that we can choose the best available model."
"Train test split provides a much better estimate of out-of-sample performance."
"A powerhouse model in eighth edition, though in the night that is far more questionable."
"Abaddon himself might be one of the single strongest Chaos Space Marine models out of any of them."
"If you're looking for overall a good balance, I think that's exactly what we get in the Escape."
"The x3 really is the benchmark in this segment."
"This is definitely a RAV4 worth considering."
"Final thoughts on the Land Rover Defender: JLR accomplished exactly what they set out to do with this SUV."
"This is so good. How much better could the C-Class be? I just don't know."
"The idea behind that is just that you have a bunch of humans who rate your model and say this thing to say is better than that thing."
"How do we know whether a model is any good? How can we know what level of confidence we ought to have in a model's output?"
"Accuracy, precision, and recall evaluate the final model."
"Determination conveys if the model is good and can be used. The R square value here is 0.3797. It means 38% of variability in Y is explained by X. The remaining 62% variation is unexplained or due to residual factors."
"If you can afford to do human evaluation, that's what you're doing when you've got your final model."
"The Bayesian view gives you the probability of the model given the data."
"The score on the training set is accuracy of 0.99 or 99% and AUC of 0.999."
"We need to know how good was our predictions; we need to evaluate our model."
"For every node that we have here, the accuracy is calculated."
"The accuracy is 79 which is considered to be good."
"Accuracy and area under the curve are essential metrics for evaluating model performance."
"Pika 1.0 is the best text-to-video model we've seen so far."
"Cross-validation is a technique which allows you to evaluate a model's performance."
"Model evaluation aims to estimate the generalization accuracy of a model on future data that is unseen or out of sample."
"We need to establish a baseline to know that our model is good."
"Cross validation is a technique which is used to train and evaluate our model on a portion of our database before re-portioning our data set and eventually using it on the new portions."
"A confusion matrix... you kind of judge the quality of your model depending on how heavy the diagonals are and how light the off diagonals are."
"F1 score is the harmonic mean of precision and recall."
"Accuracy is the worst, you probably never should use accuracy when you have class imbalance."
"I got a dice coefficient of 93 percent, which is an excellent dice coefficient, I should say."
"Getting to a validation AUC of 0.6564599, which is, I think, pretty great."
"The chi-square value would be taken as an indicator of good model fit."
"...when we evaluate the quality of our model we really should always think back, it's always a game... we have to put our hat of someone who wants to break it."
"Confusion matrix and ROC curves can be useful in shedding more light on how good the model is performing."
"Training loss and validation loss are key metrics in evaluating the performance of a model."
"The F statistic basically tells us if our model is better than nothing."
"Can we use high dimensional statistics to say not only how good is this model but how far is this model from the truth?"
"If you evaluate your model's error on a separate development set that the algorithm did not see during training, this allows you to hopefully pick a model that neither over-fits nor underfits."
"It's very important to understand regression; you'll learn about linear, non-linear, simple, multiple linear regression, and how to evaluate your model."
"The AIC and BIC account for model complexity when evaluating relative fit."
"We're going to go through how to run our logistic regression model and then how to evaluate our model."
"The classification table is really essentially looking at the correspondence between the observed group membership and group membership that's predicted based on the model."
"You need to test your model and make sure that you test the problem that you don't cheat."
"Our average precision in this case is 0.71 and our average recall is 0.715."
"We want to generate lift curves and ROC curves, which are very useful at evaluating how well our models perform."
"A good technique to prevent overfitting is always plot both curves and watch for this."
"The lower the values of AIC and SC, the better is the model."
"Putting your model in evaluation mode makes dropout a no-op and turns off the running stats for batch norm layers."
"Hold out cross-validation... you train on 70% of the data then test all your models on 30% and pick whatever has the smallest holdout cross-validation error."