diff --git a/code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb b/code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb index 1faa7d00f7bfb73787fed5fd7e9271fa416efd09..bebe8798abdbf2bef7ef48058074d91e900247e6 100644 --- a/code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb +++ b/code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.4.1\n" + "1.2.0\n" ] } ], @@ -222,8 +222,8 @@ "numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index\n", "all_features[numeric_features] = all_features[numeric_features].apply(\n", " lambda x: (x - x.mean()) / (x.std()))\n", - "# 标准化后，每个特征的均值变为0，所以可以直接用0来替换缺失值\n", - "all_features = all_features.fillna(0)" + "# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值\n", + "all_features[numeric_features] = all_features[numeric_features].fillna(0)" ] }, { @@ -234,7 +234,7 @@ { "data": { "text/plain": [ - "(2919, 354)" + "(2919, 331)" ] }, "execution_count": 8, @@ -405,12 +405,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "fold 0, train rmse 0.241054, valid rmse 0.221462\n", - "fold 1, train rmse 0.229857, valid rmse 0.268489\n", - "fold 2, train rmse 0.231413, valid rmse 0.238157\n", - "fold 3, train rmse 0.237733, valid rmse 0.218747\n", - "fold 4, train rmse 0.230720, valid rmse 0.258712\n", - "5-fold validation: avg train rmse 0.234155, avg valid rmse 0.241113\n" + "fold 0, train rmse 0.240939, valid rmse 0.221437\n", + "fold 1, train rmse 0.229326, valid rmse 0.267492\n", + "fold 2, train rmse 0.231815, valid rmse 0.237722\n", + "fold 3, train rmse 0.237550, valid rmse 0.219035\n", + "fold 4, train rmse 0.230578, valid rmse 0.258887\n", + "5-fold validation: avg train rmse 0.234042, avg valid rmse 0.240915\n" ] }, { @@ -450,10 +450,10 @@ " \n", " \n", + "\" id=\"med8f69ce34\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -489,7 +489,7 @@ " \n", " \n", " \n", - " \n", + " \n", 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a/docs/chapter03_DL-basics/3.16_kaggle-house-price.md +++ b/docs/chapter03_DL-basics/3.16_kaggle-house-price.md @@ -83,8 +83,8 @@ all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:])) numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index all_features[numeric_features] = all_features[numeric_features].apply( lambda x: (x - x.mean()) / (x.std())) -# 标准化后，每个特征的均值变为0，所以可以直接用0来替换缺失值 -all_features = all_features.fillna(0) +# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值 +all_features[numeric_features] = all_features[numeric_features].fillna(0) ``` 接下来将离散数值转成指示特征。举个例子，假设特征MSZoning里面有两个不同的离散值RL和RM，那么这一步转换将去掉MSZoning特征，并新加两个特征MSZoning\_RL和MSZoning\_RM，其值为0或1。如果一个样本原来在MSZoning里的值为RL，那么有MSZoning\_RL=1且MSZoning\_RM=0。 @@ -92,12 +92,12 @@ all_features = all_features.fillna(0) ``` python # dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征 all_features = pd.get_dummies(all_features, dummy_na=True) -all_features.shape # (2919, 354) +all_features.shape # (2919, 331) ``` -可以看到这一步转换将特征数从79增加到了354。 +可以看到这一步转换将特征数从79增加到了331。 -最后，通过`values`属性得到NumPy格式的数据，并转成`NDArray`方便后面的训练。 +最后，通过`values`属性得到NumPy格式的数据，并转成`Tensor`方便后面的训练。 ``` python n_train = train_data.shape[0]