💡 What's the difference between bagging and boosting?
Bagging and boosting are both ensemble methods, meaning they combine many weak predictors to create a strong predictor.
One key difference is that bagging builds independent models in parallel and "averages" their results in the end, whereas boosting builds models sequentially, at each step emphasizing reducing error that remains in the model by better fitting to the observations that were missed in previous steps.
💡 What's the difference between bagging and boosting?
Bagging and boosting are both ensemble methods, meaning they combine many weak predictors to create a strong predictor.
One key difference is that bagging builds independent models in parallel and "averages" their results in the end, whereas boosting builds models sequentially, at each step emphasizing reducing error that remains in the model by better fitting to the observations that were missed in previous steps.
That growth environment will include rising inflation and interest rates. Those upward shifts naturally accompany healthy growth periods as the demand for resources, products and services rise. Importantly, the Federal Reserve has laid out the rationale for not interfering with that natural growth transition.It's not exactly a fad, but there is a widespread willingness to pay up for a growth story. Classic fundamental analysis takes a back seat. Even negative earnings are ignored. In fact, positive earnings seem to be a limiting measure, producing the question, "Is that all you've got?" The preference is a vision of untold riches when the exciting story plays out as expected.
Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.
