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Machine Learning Basics Flashcards
| Front | Back |
| What is a confusion matrix | A table used in classification tasks to evaluate the performance of a model by comparing predictions to actual values. |
| What is a decision tree | A supervised learning algorithm that uses a tree-like structure to make decisions based on input features. |
| What is a feature in machine learning | An individual measurable property or characteristic used as input for a model. |
| What is a label in supervised learning | The output or target value that the model is trained to predict. |
| What is a loss function | A mathematical function that measures the error between a model's predictions and the actual values. |
| What is a neural network | A machine learning model inspired by the structure of the human brain that processes data through layers of interconnected nodes. |
| What is a random forest | An ensemble learning method that uses multiple decision trees to improve model accuracy and reduce overfitting. |
| What is a support vector machine (SVM) | A supervised algorithm that separates data into classes using a hyperplane with maximum margin. |
| What is clustering | A technique in unsupervised learning used to group similar data points together. |
| What is dimensionality reduction | A technique to reduce the number of features in a dataset while retaining important information. |
| What is gradient descent | An optimization algorithm used to minimize a model's loss function by iteratively adjusting parameters. |
| What is overfitting | When a model performs well on training data but poorly on new, unseen data. |
| What is principal component analysis (PCA) | A method used for dimensionality reduction by identifying the directions of maximum variance in the data. |
| What is reinforcement learning | A type of machine learning where an agent learns by interacting with an environment to maximize a reward signal. |
| What is supervised learning | A type of machine learning where models are trained using labeled data. |
| What is the difference between bagging and boosting | Bagging combines predictions from multiple models in parallel, while boosting builds models sequentially and focuses on improving weak learners. |
| What is the difference between classification and regression | Classification predicts discrete categories while regression predicts continuous values. |
| What is the k-Nearest Neighbors (k-NN) algorithm | A simple supervised learning algorithm that classifies new data points based on the majority class of their neighbors. |
| What is underfitting | When a model is too simple to capture the underlying patterns in the data and performs poorly. |
| What is unsupervised learning | A type of machine learning where models analyze unlabeled data to find hidden patterns or clusters. |
Front
What is overfitting
Click the card to flip
Back
When a model performs well on training data but poorly on new, unseen data.
Front
What is a neural network
Back
A machine learning model inspired by the structure of the human brain that processes data through layers of interconnected nodes.
Front
What is unsupervised learning
Back
A type of machine learning where models analyze unlabeled data to find hidden patterns or clusters.
Front
What is a feature in machine learning
Back
An individual measurable property or characteristic used as input for a model.
Front
What is the difference between bagging and boosting
Back
Bagging combines predictions from multiple models in parallel, while boosting builds models sequentially and focuses on improving weak learners.
Front
What is a confusion matrix
Back
A table used in classification tasks to evaluate the performance of a model by comparing predictions to actual values.
Front
What is the k-Nearest Neighbors (k-NN) algorithm
Back
A simple supervised learning algorithm that classifies new data points based on the majority class of their neighbors.
Front
What is underfitting
Back
When a model is too simple to capture the underlying patterns in the data and performs poorly.
Front
What is principal component analysis (PCA)
Back
A method used for dimensionality reduction by identifying the directions of maximum variance in the data.
Front
What is a support vector machine (SVM)
Back
A supervised algorithm that separates data into classes using a hyperplane with maximum margin.
Front
What is clustering
Back
A technique in unsupervised learning used to group similar data points together.
Front
What is a loss function
Back
A mathematical function that measures the error between a model's predictions and the actual values.
Front
What is a label in supervised learning
Back
The output or target value that the model is trained to predict.
Front
What is a random forest
Back
An ensemble learning method that uses multiple decision trees to improve model accuracy and reduce overfitting.
Front
What is supervised learning
Back
A type of machine learning where models are trained using labeled data.
Front
What is the difference between classification and regression
Back
Classification predicts discrete categories while regression predicts continuous values.
Front
What is dimensionality reduction
Back
A technique to reduce the number of features in a dataset while retaining important information.
Front
What is a decision tree
Back
A supervised learning algorithm that uses a tree-like structure to make decisions based on input features.
Front
What is reinforcement learning
Back
A type of machine learning where an agent learns by interacting with an environment to maximize a reward signal.
Front
What is gradient descent
Back
An optimization algorithm used to minimize a model's loss function by iteratively adjusting parameters.
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This deck covers introductory topics in machine learning, including supervised and unsupervised learning, key algorithms, and their applications in solving real-world problems.