Scikit Learn : Confusion Matrix, Accuracy, Precision and Recall By Dragonfly Statistics November 23, 2020 Data Science 29 data analytics, data science, data scientist, data scientists, data visualization, deep learning python, jupyter notebook, machine learning, matplotlib, neural networks python, nlp python, numpy python, python data, python pandas, python seaborn, python sklearn, tensor flow python, tips tricks python 20 Comments [ad_1] Scikit Learn : Confusion Matrix, Accuracy, Precision and Recall Source [ad_2] Previous article Next article Comment List Dragonfly Statistics November 23, 2020 Very bad audio Reply Dragonfly Statistics November 23, 2020 THANK YOU SO MUCH!!!!!!! Reply Dragonfly Statistics November 23, 2020 not working for more than 2 classes Reply Dragonfly Statistics November 23, 2020 Nice video sir , what does support infer in the precision, recall table Reply Dragonfly Statistics November 23, 2020 Is it possible to use TensorFlow to predict the model /object feature which concerns about measurement thanks in advance… Reply Dragonfly Statistics November 23, 2020 Nice…share jupyter notebook Reply Dragonfly Statistics November 23, 2020 thank you loads! Reply Dragonfly Statistics November 23, 2020 Great video. Could you help: How can I assign the accuracy to a variable, like a=accuracy or a = binary_accuracy. This is part of the code of the neural network: model = Sequential() model.add(Dense(48, input_dim=48, activation='relu')) model.add(Dense(24, activation='relu')) model.add(Dense(2, activation='sigmoid')) model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'] model.fit(training_data, target_data, epochs=1000) scores = model.evaluate(training_data, target_data) "training_data, target_data are arrays" Result of evaluation: binary_accuracy: 0.5000 binary_accuracy: 50.00% Reply Dragonfly Statistics November 23, 2020 Hi,How can I explain to some accuracy score to someone. I mean if it 95%,how I can explain it and wht is it indicating? Please explain it Reply Dragonfly Statistics November 23, 2020 not audible.Cannot hear your voice Reply Dragonfly Statistics November 23, 2020 why the avg/total is 93 in precision Reply Dragonfly Statistics November 23, 2020 I think 347 should be the false positive and 263 should be the false negative. correct me if I'm wrong. Reply Dragonfly Statistics November 23, 2020 hi, I'm having this error:ValueError: Classification metrics can't handle a mix of multiclass and continuous targetsany idea how to rectify it? Reply Dragonfly Statistics November 23, 2020 Very helpfull!! Reply Dragonfly Statistics November 23, 2020 how to reduce the model score value i am getting it as 1.0 for my dataset how can i reduce it? Reply Dragonfly Statistics November 23, 2020 in classification report i got all the values as zeroes for class 0 and some values for class 1(precision,recall,f1) how do i correct this Reply Dragonfly Statistics November 23, 2020 i have a [[0,34],[0,61]] in confusion matrix how do i interpret this as? and if its wrong how do i correct this Reply Dragonfly Statistics November 23, 2020 great video lad Reply Dragonfly Statistics November 23, 2020 Very nicely explained thank you sir! Reply Dragonfly Statistics November 23, 2020 hey bro! do you want 10000 viewers from my country? contact me if interested at my email ms9678204@gmail.com Reply Write a comment Cancel reply Save my name, email, and website in this browser for the next time I comment.
Very bad audio
THANK YOU SO MUCH!!!!!!!
not working for more than 2 classes
Nice video sir , what does support infer in the precision, recall table
Is it possible to use TensorFlow to predict the model /object feature which concerns about measurement thanks in advance…
Nice…share jupyter notebook
thank you loads!
Great video. Could you help: How can I assign the accuracy to a variable, like a=accuracy or a = binary_accuracy. This is part of the code of the neural network:
model = Sequential()
model.add(Dense(48, input_dim=48, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['binary_accuracy']
model.fit(training_data, target_data, epochs=1000)
scores = model.evaluate(training_data, target_data)
"training_data, target_data are arrays"
Result of evaluation:
binary_accuracy: 0.5000
binary_accuracy: 50.00%
Hi,
How can I explain to some accuracy score to someone.
I mean if it 95%,how I can explain it and wht is it indicating?
Please explain it
not audible.Cannot hear your voice
why the avg/total is 93 in precision
I think 347 should be the false positive and 263 should be the false negative. correct me if I'm wrong.
hi, I'm having this error:
ValueError: Classification metrics can't handle a mix of multiclass and continuous targets
any idea how to rectify it?
Very helpfull!!
how to reduce the model score value
i am getting it as 1.0 for my dataset
how can i reduce it?
in classification report i got all the values as zeroes for class 0 and some values for class 1(precision,recall,f1) how do i correct this
i have a [[0,34],[0,61]] in confusion matrix how do i interpret this as? and if its wrong how do i correct this
great video lad
Very nicely explained thank you sir!
hey bro! do you want 10000 viewers from my country? contact me if interested at my email ms9678204@gmail.com