sklearn中的classification_report函數用於顯示主要分類指標的文本報告．在報告中顯示每個類的精確度，召回率，F1值等信息。
主要參數:
y_true：1維數組，或標簽指示器數組/稀疏矩陣，目標值。
y_pred：1維數組，或標簽指示器數組/稀疏矩陣，分類器返回的估計值。
labels：array，shape = [n_labels]，報表中包含的標簽索引的可選列表。
target_names：字符串列表，與標簽匹配的可選顯示名稱（相同順序）。
sample_weight：類似於shape = [n_samples]的數組，可選項，樣本權重。
digits：int，輸出浮點值的位數．
Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : array, shape = [n_labels] Optional list of label indices to include in the report. target_names : list of strings Optional display names matching the labels (same order). sample_weight : array-like of shape = [n_samples], optional Sample weights. digits : int Number of digits for formatting output floating point values Returns ------- report : string Text summary of the precision, recall, F1 score for each class. The reported averages are a prevalence-weighted macro-average across classes (equivalent to :func:`precision_recall_fscore_support` with ``average='weighted'``). Note that in binary classification, recall of the positive class is also known as "sensitivity"; recall of the negative class is "specificity". Examples -------- >>> from sklearn.metrics import classification_report >>> y_true = [0, 1, 2, 2, 2] >>> y_pred = [0, 0, 2, 2, 1] >>> target_names = ['class 0', 'class 1', 'class 2'] >>> print(classification_report(y_true, y_pred, target_names=target_names)) precision recall f1-score support <BLANKLINE> class 0 0.50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67 0.80 3 <BLANKLINE> avg / total 0.70 0.60 0.61 5 <BLANKLINE>
參考：
https://www.programcreek.com/python/example/81623/sklearn.metrics.classification_report
https://blog.csdn.net/akadiao/article/details/78788864