Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.
Bias refers to systematic errors in data collection or analysis that can lead to incorrect conclusions.
Model evaluation is crucial to assess the performance and accuracy of a model on unseen data.
A subset of artificial intelligence (AI) that enables systems to learn from data and improve performance over time without explicit programming.
They derive knowledge from data in order to make predictions.
By figuring out the best choice through a series of questions.
When expertise is lacking, such as navigating on Mars.
Because human knowledge is difficult to articulate.
A type of machine learning where the model is trained on labeled data, with each training example paired with an output label.
When changing X affects the outcome or when aiming to determine how X affects Y or predict Y.
Machine Learning is a subfield of Artificial Intelligence.
Different ML algorithms encode different ways to generalize from a data set.
Predicting how much a house will sell for based on its size.
The association or co-relationship between two variables.
When the dependent variable is categorical with binary outputs.
They ensure models generalize well to unseen data.
Model evaluation is crucial to assess the performance and accuracy of a model on unseen data.
Natural groupings based on similarities.
To create maximal distance between two classes of data points.
Customer segmentation.
Relationships between two things.
Predicting the risk of heart attack.
Bias refers to systematic errors in data collection or analysis that can lead to incorrect conclusions.
How the process used to select a data set can introduce biases into later analysis.
It leads to better model performance.
Unlabeled data or data of unknown structure.
No, there is no one best ML algorithm that outperforms all others across all possible data sets.
Which products are frequently bought together.
When swapping X and Y gives the same result or when analyzing if there is a relationship between X and Y.
A type of machine learning where the model is trained on labeled data, with each training example paired with an output label.
It identifies the data and provides the desired response, such as recognizing an apple.
Predicting whether a person is happy or sad.
Regression and Classification.
To tune the model's hyperparameters and assess how well the model generalizes to unseen data.
To predict the categorical class labels of new instances based on past observations.
The final performance of the model after training and validation, providing an unbiased assessment.
Predicting a fruit type based on factors like colour, size, and place of origin.
Yes, it is flexible enough for both.
To predict outcomes based on input data using labeled examples.
Yes, it is often used for clustering tasks.
Outliers and noise in the data.
It is essential for building reliable machine learning systems.
A popular method used in unsupervised learning for grouping data into clusters.
In personalized medicine.
It assesses how well a machine learning model performs on unseen data.
It improves accuracy and reduces the chance of overfitting.
A type of clustering method used in unsupervised learning.
Data is used to train models, allowing them to make predictions or decisions without explicit programming.
It ensures that the model performs well on new, unseen data.
Unlabeled data or data of unknown structure.
To discover hidden patterns in data without the need for human intervention.
They are prone to overfitting.
Useful for exploratory data analysis.
Results can be difficult to interpret without clear labels.
The strength of a linear relationship between two numeric attributes.
To learn from labeled data to predict outcomes.
A simple way to make decisions based on different criteria by asking a series of questions.
Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.
To train a model to identify specific categories, such as distinguishing between an apple and another fruit.
A supervised learning model typically used for data classification problems.
A supervised machine learning algorithm.
An exploratory data analysis technique that organizes information into meaningful subgroups without knowing group memberships.
By using predefined rules and logic to process inputs and produce outputs.
Because analysis relies on vast amounts of data.
To discover hidden patterns in data without the need for human intervention.
A type of machine learning where the model is trained on labeled data, with each training example paired with an output label.
Supervised learning uses labeled data, while unsupervised learning deals with unlabeled data.
Decision Trees, Support Vector Machines (SVM), and Random Forest.
A type of machine learning where the model is trained on unlabeled data to find patterns.
Yes, it is commonly used for classification tasks.
Labeled data.
Specific predictions or classifications.
A technique used in retail to understand the purchasing behavior of customers.
'Yes' and 'No' or 'True' and 'False'.
To predict continuous outcomes by finding relationships between variables.
To train the machine learning model and allow it to learn from the data.
Unlabeled data or data of unknown structure.
To group similar items together, such as apples and pears that look alike.
Classifying emails as either 'spam' or 'not spam'.
By putting similar items into the same groups.
Linear regression and logistic regression.
Machine learning can handle complex patterns and large datasets more effectively than traditional programming.
A wide range of algorithms is available for different tasks.
The strength of association between two attributes.
K-Means Clustering.
When labeled data is available and specific predictions are needed.
Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.
In traditional programming, rules are explicitly coded, while in machine learning, the system learns from data.
Bias refers to systematic errors in data collection or analysis that can lead to incorrect conclusions.
Grouping different fruits based on features like species and color.
By combining multiple decision trees.
The presence of certain words or the sender.
An example of an association method used to find relationships between items in transactions.
Clear interpretation of model results.
To discover hidden structures or patterns in data without predefined labels.
Choosing the correct number of clusters (K) can be challenging.
Unlabeled data to identify patterns.
Model evaluation helps determine the effectiveness and accuracy of a model in making predictions.
It encodes a linear generalization from the data and ignores nonlinear relationships.
An unsupervised learning method that finds relationships between variables in a dataset.
The dataset the algorithm runs on and the choice of algorithm.
High accuracy with sufficient labeled data.
It requires large amounts of labeled data, which can be time-consuming and expensive to obtain.
It can uncover hidden patterns and insights in data.
Unlabeled data.
Predicting house prices.
Groups data points or identifies structure without specific outputs.
To ensure generalization to new data, identify strengths and weaknesses of the model, and guide model selection and improvement.
Supervised learning uses labeled data, while unsupervised learning deals with unlabeled data.
Into three parts: training, validation, and test datasets.
Supervised learning uses labeled data, while unsupervised learning deals with unlabeled data.
No need for labeled data, simplifying data preparation.
From -1 to +1.
Linear Regression.
To discover hidden patterns in data without the need for human intervention.
How to numerically relate an independent variable to a dependent variable.
Yes, machine learning models can improve and adapt as they are exposed to more data.
It identifies the strengths and weaknesses of different models, guiding the selection process.
A type of machine learning where the model is trained on labeled data.
It may struggle with imbalanced datasets.
The letter r.
A dataset with input-output pairs (features and labels).
For exploring data and discovering hidden patterns without predefined labels.
