Why is accuracy not a good measure for classification problems?

Accuracy and error rate are the de facto standard metrics for summarizing the performance of classification models. Classification accuracy fails on classification problems with a skewed class distribution because of the intuitions developed by practitioners on datasets with an equal class distribution.

How do you increase F1 score in imbalanced dataset?

How to improve F1 score for classification

1. StandardScaler()
2. GridSearchCV for Hyperparameter Tuning.
3. Recursive Feature Elimination(for feature selection)
4. SMOTE(the dataset is imbalanced so I used SMOTE to create new examples from existing examples)

How do you measure the performance of a classification model?

Logarithmic loss (or log loss) measures the performance of a classification model where the prediction is a probability value between 0 and 1. Log loss increases as the predicted probability diverge from the actual label. Log loss is a widely used metric for Kaggle competitions.

Does a high accuracy for the model mean it is good?

Accuracy counts all of the true predicted values, but not specific for each label that exists. This is a serious concern if your goal wants to predict a specific label, for example, a positive label, correctly. Higher accuracy doesn’t mean that we have a good performance on predicting a specific label.

What is the main disadvantage of using accuracy to evaluate models?

Problem with accuracy: It hides the detail you need to better understand the performance of your classification model. You may achieve an accuracy of 95\% or more but is not a good score if 95 records for every 100 belong to one class as you can achieve this score by always predicting the most common class value.

What is accuracy in classification model?

Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right. Formally, accuracy has the following definition: Accuracy = Number of correct predictions Total number of predictions.

What is the difference between accuracy and F1 score?

Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial. In most real-life classification problems, imbalanced class distribution exists and thus F1-score is a better metric to evaluate our model on.

Why is the F1 score so low?

A low F1 score is an indication of both poor precision and poor recall. The machine-learning annotator generates erroneous annotations and fails to find annotations that it should have found.

How do you evaluate the accuracy of a classifier?

You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier.

How is accuracy calculated in machine learning?

Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions.

How do you define classification accuracy?

Classification accuracy, which measures the number of correct predictions made divided by the total number of predictions made, multiplied by 100 to turn it into a percentage.

Is 80\% a good accuracy?

If your ‘X’ value is between 70\% and 80\%, you’ve got a good model. If your ‘X’ value is between 80\% and 90\%, you have an excellent model. If your ‘X’ value is between 90\% and 100\%, it’s a probably an overfitting case.

What is false positive rate in classification?

First of all False positive rate is a parameter of error metric derived from confusion matrix. Confusion matrix depends on distinct respective model. Thus, each classification model will have different confusion matrix which turns out to have different False positive rate may be low or high as compared to previous model.

Should we consider only accuracy as a performance measure?

Answer:- In my opinion, we should not consider only accuracy as a performance measure as it evaluate only true positive , true Negative and sum total of a model. We have a many performance measures like recall, precision and f1-score. All these metrics are well explained using a confusion matrix in previous question.

How to determine which machine learning model is best fit?

Forest, etc and check false positive rate using confusion matrix for each of the models. On comparison we can conclude which machine learning model or statistical model is best fit having high accuracy and lowest possible false positive rate. A Machine learning paradigm known as ensemble learning can also be used in this condition.