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random_test[r.permutation(len(x))[:60]]=True\ngroup_test=groups>=9\ndef compare(test):\n    train=~test; means={g:y[train&(groups==g)].mean() for g in np.unique(groups[train])}\n    memorized=np.array([means.get(g,y[train].mean()) for g in groups[test]])\n    X=np.c_[x,np.ones(len(x))]; w=np.linalg.lstsq(X[train],y[train],rcond=None)[0]\n    rmse=lambda p:float(np.sqrt(np.mean((p-y[test])**2)))\n    return {'ntrain':int(train.sum()),'ntest':int(test.sum()),'overlapping_groups':len(set(groups[train])&set(groups[test])),'batch_mean_RMSE':rmse(memorized),'x_only_RMSE':rmse(X[test]@w)}\nprint('random sample split',compare(random_test)); print('new batch split',compare(group_test))\nassert compare(group_test)['overlapping_groups']==0\nprint('claim tested: does low random-split error predict performance in unseen acquisition groups?')\n```","sha256":"d8a3a5c9264688702b427dcc23aa1d7ab143436d75ce9c4ee4a73e1353d133f2","chapter":{"id":"course","title":"全讲收束：你的“新样本”究竟新在哪里？ · 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