我的實驗有什麼問題（試圖預測汽車銷售）？

Question

我有這樣的數據集（只是它的樣品）：使用Azure上ML以下實驗

DATE_REF,MONTH,YEAR,DAY_OF_YEAR,DAY_OF_MONTH,WEEK_DAY,WEEK_DAY_1,WEEK_DAY_2,WEEK_DAY_3,WEEK_DAY_4,WEEK_DAY_5,WEEK_DAY_6,WEEK_DAY_7,WEEK_NUMBER_IN_MONTH,WEEKEND,WORK_DAY,AMOUNT_SOLD 
20100101,1,2010,1,1,6,0,0,0,0,0,1,0,1,0,0,0 
20100102,1,2010,2,2,7,0,0,0,0,0,0,1,1,1,0,2 
20100103,1,2010,3,3,1,1,0,0,0,0,0,0,2,1,0,0 
20100104,1,2010,4,4,2,0,1,0,0,0,0,0,2,0,1,12830 
20100105,1,2010,5,5,3,0,0,1,0,0,0,0,2,0,1,19200 
20100106,1,2010,6,6,4,0,0,0,1,0,0,0,2,0,1,22930 
20100107,1,2010,7,7,5,0,0,0,0,1,0,0,2,0,1,23495 
20100108,1,2010,8,8,6,0,0,0,0,0,1,0,2,0,1,23215 
20100109,1,2010,9,9,7,0,0,0,0,0,0,1,2,1,0,172 
20100110,1,2010,10,10,1,1,0,0,0,0,0,0,3,1,0,0 
20100111,1,2010,11,11,2,0,1,0,0,0,0,0,3,0,1,18815 
20100112,1,2010,12,12,3,0,0,1,0,0,0,0,3,0,1,25415 
20100113,1,2010,13,13,4,0,0,0,1,0,0,0,3,0,1,25262 
20100114,1,2010,14,14,5,0,0,0,0,1,0,0,3,0,1,27967 
20100115,1,2010,15,15,6,0,0,0,0,0,1,0,3,0,1,26352 
20100116,1,2010,16,16,7,0,0,0,0,0,0,1,3,1,0,202 
20100117,1,2010,17,17,1,1,0,0,0,0,0,0,4,1,0,10 
20100118,1,2010,18,18,2,0,1,0,0,0,0,0,4,0,1,20295 
20100119,1,2010,19,19,3,0,0,1,0,0,0,0,4,0,1,25982 
20100120,1,2010,20,20,4,0,0,0,1,0,0,0,4,0,1,24745 
20100121,1,2010,21,21,5,0,0,0,0,1,0,0,4,0,1,28087 
20100122,1,2010,22,22,6,0,0,0,0,0,1,0,4,0,1,28417 
20100123,1,2010,23,23,7,0,0,0,0,0,0,1,4,1,0,115 
20100124,1,2010,24,24,1,1,0,0,0,0,0,0,5,1,0,5 
20100125,1,2010,25,25,2,0,1,0,0,0,0,0,5,0,1,20185 
20100126,1,2010,26,26,3,0,0,1,0,0,0,0,5,0,1,25932 
20100127,1,2010,27,27,4,0,0,0,1,0,0,0,5,0,1,31710 
20100128,1,2010,28,28,5,0,0,0,0,1,0,0,5,0,1,21020 
20100129,1,2010,29,29,6,0,0,0,0,0,1,0,5,0,1,51460 
20100130,1,2010,30,30,7,0,0,0,0,0,0,1,5,1,0,670 
20100131,1,2010,31,31,1,1,0,0,0,0,0,0,6,1,0,17 

我試圖預測AMOUNT_SOLD新的日期（DATE_REF）：

然後我部署了Web服務並測試了預測，但我得到的所有列都是零，即AMOUNT_SOLD列。

我可能會錯過什麼？

Answer 1

儘管我想複製Azure ML實驗，但我沒有足夠的數據。但是我做了什麼如下：

我複製你的樣本數據，然後用4倍乘以它（添加排x 2）。 然後分割數據（70％/ 30％），隨機種子7（用於可重現的結果）。 增強型決策樹迴歸具有默認參數。 On Tune Model HyperParameters，I selected AMOUNT_SOLD作爲標籤列。 然後得分模型和評估模型。

精度/判定係數是相當不錯的。

之後，要將其作爲Web服務進行部署，您必須首先從訓練實驗中設置預測實驗。 Setup Web Service > Predictive Experiment你的實驗會像魔術一樣移動。

的Web服務輸入模塊將默認在實驗頂部擺放。我移動它並連接在Score Model的右側，這樣當您輸入Web服務的參數時，將使用您的訓練模型預測它。

的分數模型模塊後，我把一個選擇列在數據集模塊和選擇只有一個名爲打進標籤列。此列包含模型的預測。然後，我使用編輯元數據模塊重命名Scored Labels列，然後將其傳遞到Web服務輸出模塊。

您的實驗現在已準備好部署爲Web服務。

要預測新值，我使用當前日期詳細信息作爲輸入測試了Web服務。（雖然DATE_REF輸入必須是20170818：d）

然後輸出看起來是這樣的：現在

您的Web服務可以預測新值。