Iam pretty new to the whole topic so please dont be harsh. I know these may be simple questions but everybody has to start somewhere ^^
So I created (or more copied) my first little Model which predicts sons heights based on their fathers.
#Father Data X=data['Father'].values[:,None] X.shape #According sons data y=data.iloc[:,1].values y.shape #Spliting the data into test and train data X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) #Doing a linear regression lm=LinearRegression() lm.fit(X_train,y_train) # save the model to disk filename = 'Father_Son_Height_Model.pckl' pickle.dump(lm, open(filename, 'wb')) #Predicting the height of Sons y_test=lm.predict(X_test) print(y_test)
Now I wanted to create a plot that displays the accuracy of my model or how "good" it is. Something like it is done here: https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
But i cant quiet get it to work. This here Sems like I would work but what should I store in "model_history"?
plt.subplot(212) plt.title('Accuracy') plt.plot(model_history.history['acc'], label='train') plt.plot(model_history.history['val_acc'], label='test') plt.legend() plt.show()
A easy to adapt tutorial-Link would be a geat help already. Keras seems to be a thing but I would want to avoid yet another library if possible and sensible.