GitHub - facebookresearch/Detectron: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.
GitHub - nejumi/kaggle_memo
まずは、素うどんのXGBoostにかけて、plot_importance, feature_importances_を確認する。しかる後に、各特徴量をF-SCOREの高い順にExploratory Data Analysis (EDA)を行い、データに対する感覚を掴む。特徴量の数が少ないのであれば、初めからEDA。 情報を含まないcolumnsを除く。[Kaggle Kernel: R, Python] 標準偏差が0の説明変数 (constant cols) を除く。 重複した説明変数 (duplicated cols) を1つだけ残して他を除く。 相関係数が1である説明変数の組 (perfectly correlated cols) を探し、1つだけ残して他を除く。 各列について、値が0である説明変数の数を数えて、合計値を追加の説明変数として加える (count 0 per row)。逆
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