Chapter 7-10 Flashcards
the study that give computer the ability to learn without being explicitly programmed.
Machine Learning
defined by its use of labeled datasets to train algorithms to classify data or predict outcome accurately.
Supervised Learning
2 Supervised Learning Techniques:
- Linear Regression
- Logistic Regression
uses to predict value based on the value of another variable.
Linear Regression
same as linear, but the predicted value is logical.
Logistic Regression
uses unlabeled data. It discovers patterns that help solve for clustering or association problems.
Unsupervised Learning
2 Unsupervised Learning Techniques:
- Clustering
- Association
similar to supervised learning, but the algorithm isn’t trained using sample data. The model learns as it goes using trial and error.
Reinforced Learning
2 Reinforced Learning Techniques:
- Positive Reinforcement
- Negative Reinforcement
a technique of reinforced learning that uses rewards for correct action
Positive Reinforcement
a technique of reinforced learning that uses punishment for incorrect action
Negative Reinforcement
is a process that models the relationship between two variables using a linear equation through data observation.
Linear Regression
is a measure of how well a linear regression model fits the data.
R-Squared
is a statistical measure of how close the data are to the fitted regression.
R- Squared Value
For R-shared method, are good value, these are values close to 1
High Squared Value
For R-shared method, is not a good value, the actual value is far from the predicted value.
Low Squared Value
regression with multiple independent variables.
Multi-Variate Regression
is the process when preparation is necessary in order to make the dataset useable for the purpose.
Data Cleaning
a classification technique based on Bayes’ Theoren with an assumption of independence among predicators.
Naïve Bayes Algorithm
the probability of an event, based on prior knowledge of conditions that might be related to the event.
Bayes Theorem
proposed Bayes Theorem
Thomas Bayes
it is a method of quantization that is aimed to partition (n) a collection into a number (k) of cluster with the nearest mean.
K-Means Clustering
refers to a collection of data points aggregated together because of certain similarities.
Cluster
identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.
K-Means Algorithm
refers to averaging the data; that is finding the centroid.
Means
