|《machine-learning-mindmap》 4 Concepts|
When we are interested mainly in the predicted variable as a result of the inputs, but not
on the each way of the inputs affect the prediction. In a real estate example, Prediction
would answer the question of: Is my house over or under valued? Non-linear models are
very good at these sort of predictions, but not great for inference because the models
are much less interpretable.
When we are interested in the way each one of the inputs affect the prediction. In a real
estate example, Prediction would answer the question of: How much would my house
cost if it had a view of the sea? Linear models are more suited for inference because the
models themselves are easier to understand than their non-linear counterparts.
Fraction of correct predictions, not reliable as skewed when the
data set is unbalanced (that is, when the number of samples in
different classes vary greatly)
Out of all the examples the classifier labeled as
positive, what fraction were correct?
Out of all the positive examples there were, what
fraction did the classifier pick up?
Harmonic Mean of Precision and Recall: (2 * p * r /(p + r))
ROC Curve - Receiver Operating
True Positive Rate (Recall / Sensitivity) vs False Positive
Bias refers to the amount of error that is introduced by approximating
a real-life problem, which may be extremely complicated, by a simple
model. If Bias is high, and/or if the algorithm performs poorly even on
your training data, try adding more features, or a more flexible model.
Variance is the amount our model’s prediction would
change when using a different training data set. High:
Remove features, or obtain more data.
Goodness of Fit = R^2
1.0 - sum_of_squared_errors / total_sum_of_squares(y)
Mean Squared Error (MSE)
The mean squared error (MSE) or mean squared deviation
(MSD) of an estimator (of a procedure for estimating an
unobserved quantity) measures the average of the squares
of the errors or deviations—that is, the difference between
the estimator and what is estimated
The proportion of mistakes made if we apply
out estimate model function the the training
observations in a classification setting.
One round of cross-validation involves partitioning a sample of data into complementary subsets,
performing the analysis on one subset (called the training set), and validating the analysis on the
other subset (called the validation set or testing set). To reduce variability, multiple rounds of
cross-validation are performed using different partitions, and the validation results are averaged
over the rounds.
Repeated random sub-sampling validation