Below are my questions of R-Squared for real valued label under non linear regression learner. It may be a large problem, if there is no easy answer, could you give me some references？ Firstly for the real valued label, except for R-squared, is there any good value to evaluate the performance of fitting? I know that small MSE, MAE, e.t.c usually mean the good fitting. However they may be not as intuitive as a ratio like R-Sqaured (how small is …

I'm trying to build model for this datatset (Age prediction): The input image has the shape: 3, 128, 128 and the predicted labels (ages) range between 20 to 51. I want to build model and train it with MSE and R2 metrics. I built the following model: def GetPretrainedModel(): oModel = torchvision.models.resnet50(pretrained=True) for mParam in oModel.parameters(): if False == isinstance(mParam, nn.BatchNorm2d): mParam.requires_grad = False dIn = oModel.fc.in_features oModel.fc = nn.Sequential( nn.Linear(dIn, 512), nn.ReLU(), nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, …

If I train a model following a random search, (and in general for this problem I am working on), a big batch size seems to control R2 score where bs=200 or more, say, roughly, gives R2 scores of 0.95 or above and an MSE or about 0.012. If I lower the batch size, MSE may decrease a little faster (I think) except that R2 score blows up. (to minus -5692.7026, say and thereabouts). E.g. 97256/100664 [===========================>..] - ETA: 6s - …

I have a densely connected NN and I'm running a hyper parameter optimisation for multi-target output. During hyper parameter optimisation training, each epoch KerasTuner focuses on val_loss. During training I can see that I have absurdly large negative R2 values (basically a terribly fitted model), that decrease to 0 (and hopefully continue to 1) mostly whilst MSE drops too. Occasionally I'll get extremely large (negative) jumps back up in the R2_val score, whilst all other metrics decrease. (including R2_train score) …

I have a sample time-series dataset (23, 14291) a pivot table count for 24hrs for some users. After pre-processing, I have a dataset with (23, 200) shape. I filtered some of the columns/features which don't have a time-series based nature to reach/keep meaningful columns/features by PCA method to keep those with a high amount of data variance or correlation matrix to exclude highly correlated columns/features. I took advantage of MultiOutputRegressor() and predicted all columns for a certain range of time …

I am training an XGBoost model, xgbr, using xgb.XGBRegressor() with 13 features and one numeric target. The R2 on the test set is 0.935, which is good. I am checking the feature importance by for col,score in zip(X_train.columns,xgbr.feature_importances_): print(col,score) When I check the importance type by xgbr.importance_type, the result is gain. I have a feature, x1, whose importance seems to be 0.0068, not so high. x1 is a categorical feature with a cardinality of 5122, and I apply LabelEncoder before …

I'm trying to create a linear regression model with use of PolynomialFeatures. But when I evaluate it, I get really strange scores. I know that R^2 can be applied to this model and I think I've trying everything. I'd really apricate a good advice. Here is my code. X = df_all[['Elevation_gain', 'Distance']] y = df_all['Avg_tempo_in_seconds'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) for n in range(2,10,1): poly_feat = PolynomialFeatures(degree=n, include_bias = True) X_poly_train = …

I am trying to determine which model result is better. Both results are trying to achieve the same objective, the only difference is the exact data that is being used. I used random forest, xgboost, and elastic net for regression. Here is one of the results that has low rmse but not so good r2 model n_rows_test n_rows_train r2 rmse rf 128144 384429 0.258415240861579 8.44255341472637 xgb 128144 384429 0.103772500839367 9.28116624462333 e-net 128144 384429 0.062460300392487 9.49266713837073 The other model run has …

Does the appliance of R-squared to non-linear models depends on how we calculate it? $R^2 = \frac{SS_{exp}}{SS_{tot}}$ is going to be an inadequate measure for non-linear models since an increase of $SS_{exp}$ doesn't necessarily mean that the variance is decreasing, but if we calculate it as $R^2 = 1 - \frac{SS_{res}}{SS_{tot}}$, then it's as much meaningful for non-linear models as it is for linear ones. I asked a similar question here where I showed that R-squared is no worse for …

Q) We want to learn a function f(x) of the form f(x) = ax + b which is parameterized by (a, b). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss. (a) (4, 3) (b) (1, 4) (c) (4, 1) (d) (3, 4)

I'm running a regression model on a pretty large data set and getting a fairly woeful $R^2$ score of ~0.2 (see plot below), despite the plot looking like the model is generally pointing in the right direction. My question is, when you have over a million data points, how high can you realistically expect the $R^2$ to go in real world data with a decent amount of noise? What prompts by scepticism of such traditional measures are articles such as …

Does statsmodels compute R2 and other metrics on a validation set? I am using the OLS from the statsmodels.api when printing summary, an r2 and r2_asjusted are presented. I did not trust those 0.88 and computed an own adjusted R2 with scikit-learn r2_score and the adjusted r2 function from this answer resulting in 0.88 as well. So the question arose.