Partial Dependence Plots：部分依賴圖
模型訓練后(fit)，才可以創建部分依賴圖(PDP)
部分依賴圖，反映出了某一列特征，對目標列（target）的影響
例子：
from sklearn.ensemble.partial_dependence import partial_dependence, plot_partial_dependence
# get_some_data is defined in hidden cell above.
X, y = get_some_data()
# scikit-learn originally implemented partial dependence plots only for Gradient Boosting models
# this was due to an implementation detail, and a future release will support all model types.
my_model = GradientBoostingRegressor()
# fit the model as usual
my_model.fit(X, y)
# Here we make the plot
my_plots = plot_partial_dependence(my_model,
features=[0,1,2], # column numbers of plots we want to show
X=X, # raw predictors data.
feature_names=['Distance', 'Landsize', 'BuildingArea'], # labels on graphs
grid_resolution=10) # number of values to plot on x axis 代表橫坐標上點的數量，越大表示圖上的點越多，默認為100
部分依賴圖(PDP)使用提示：
一次最多顯示2-3個變量，太多了看不清
grid_resolution參數不要太大，否則圖形的鋸齒狀明顯，可以像例子一樣設置為10
還有一個方法叫partial_dependence，這個可以返回構成PDP的原始數據，可以用Seaborn等圖形包畫出更好看的圖