title: How to fix this classification report warning?
body: <p>I created a model for multiclass classification. Everything went good, got a validation accuracy of 84% but when I printed the classification report I got this warning:</p>
  <pre><code> UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
    _warn_prf(average, modifier, msg_start, len(result))
  </code></pre>
  <p>classification report:</p>
  <pre><code>              precision    recall  f1-score   support
  
             0       0.84      1.00      0.91     51890
             1       0.67      0.04      0.08      8706
             2       0.00      0.00      0.00      1605
  
      accuracy                           0.84     62201
     macro avg       0.50      0.35      0.33     62201
  weighted avg       0.79      0.84      0.77     62201
  </code></pre>
  <p>Source Code -</p>
  <pre><code>import pandas as pd
  
  df=pd.read_csv('Crop_Agriculture_Data_2.csv')
  df=df.drop('ID',axis=1)
  
  dummies=pd.get_dummies(df[['Crop_Type', 'Soil_Type', 'Pesticide_Use_Category', 'Season']],drop_first=True)
  df=df.drop(['Crop_Type', 'Soil_Type', 'Pesticide_Use_Category', 'Season'],axis=1)
  df=pd.concat([df,dummies],axis=1)
  
  df['Crop_Damage']=df['Crop_Damage'].map({'Minimal Damage':0,'Partial Damage':1,'Significant Damage':2})
  
  x=df.drop('Crop_Damage',axis=1).values
  y=df.Crop_Damage.values
  from sklearn.model_selection import train_test_split
  x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.3,random_state=101)
  
  from sklearn.preprocessing import MinMaxScaler
  mms=MinMaxScaler()
  x_train=mms.fit_transform(x_train)
  x_test=mms.transform(x_test)
  
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense,Dropout,Flatten
  
  model=Sequential()
  model.add(Flatten())
  model.add(Dense(10,activation='relu'))
  model.add(Dropout(0.3))
  model.add(Dense(6,activation='relu'))
  model.add(Dropout(0.3))
  model.add(Dense(3,activation='softmax'))
  
  model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
  model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=13)
  
  import numpy as np
  pred=np.argmax(model.predict(x_test),axis=-1)
  
  from sklearn.metrics import classification_report
  print(classification_report(y_test,pred))
  </code></pre>
  <p>I think it might be because most of the data is in one category but I'm not sure. Is there anything I can do to solve this ?</p>