The most convenient goodness-of-fit for random forest is out-of-bag cross-validation, it can provide a R² value and e.g. std.dev of prediction. It is important to use cross-validation, as the direct goodness-of-fit is completely misleading for any non-linear machine learning model, as they can fit mostly anything also noise. Here's a link on the interpretation of R² for the randomForest package: Link!

p-values are not much used in random forest context, as the hypothesis space is so huge.

If you want to identify related variables,use variable importance. If you want to make a fair comparison on prediction performance between MLR an RF you need to design a cross-validation and embed both models. 10-fold and 10 times repeated would normally be regarded as solid. From such a cross validation a R² and std.error could be extracted.

The most convenient goodness-of-fit for random forest is out-of-bag cross-validation, it can provide a R² value and e.g. std.dev of prediction. It is important to use cross-validation, as the direct goodness-of-fit is completely misleading for any non-linear machine learning model, as they can fit mostly anything also noise. Here's a link on the interpretation of R² for the randomForest package: Link!

p-values are not much used in random forest context, as the hypothesis space is so huge.

If you want to identify related variables,use variable importance. If you want to make a fair comparison on prediction performance between MLR an RF you need to design a cross-validation and embed both models. 10-fold and 10 times repeated would normally be regarded as solid. From such a cross validation a R² and std.error could be extracted.

The most convenient goodness-of-fit random forest is out-of-bag cross-validation, it can can provide a R² value and e.g. std.dev of prediction. It is important to use cross-validation as the direct goodness-of-fit is completely misleading for any non-linear machine learning model, as they can fit anything also noise. Here's a link on the interpretation of R² for the randomForest package: enter link description here

p-values are not much used in random forest context, as the hypothesis space is so huge.

If you want to identify variable, use variable importance. If you want to make a fair comparison on prediction performance between MLR an RF you need to design a cross-validation and embed both model. 10-fold and 10 times repeated would normally be regarded as solid. From such a cross validation a R² and std.error could be extracted.

The most convenient goodness-of-fit for random forest is out-of-bag cross-validation, it can provide a R² value and e.g. std.dev of prediction. It is important to use cross-validation, as the direct goodness-of-fit is completely misleading for any non-linear machine learning model, as they can fit mostly anything also noise. Here's a link on the interpretation of R² for the randomForest package: Link!

p-values are not much used in random forest context, as the hypothesis space is so huge.

If you want to identify related variables,use variable importance. If you want to make a fair comparison on prediction performance between MLR an RF you need to design a cross-validation and embed both models. 10-fold and 10 times repeated would normally be regarded as solid. From such a cross validation a R² and std.error could be extracted.