Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

The Data Science Lifecycle involves defining the problem, collecting and preparing data, exploring and analyzing data, building models, evaluating performance, and deploying solutions.

In supervised learning, the model is trained on a labeled dataset, while unsupervised learning involves working with unlabeled data, allowing the algorithm to find patterns on its own.

Regularization is a technique to prevent overfitting in a model by adding a penalty term to the loss function. It helps in generalizing the model to new, unseen data.

The curse of dimensionality refers to the challenges and increased complexity that arise when working with high-dimensional data. It can lead to overfitting and increased computational requirements.

Variance measures the model\\\\\\\'s sensitivity to fluctuations in the training data, while bias measures the model\\\\\\\'s deviation from the true values. Balancing both is crucial for a well-performing model.

Feature engineering involves transforming raw data into a format that is more suitable for model training. It is crucial because the choice of features significantly impacts the model\\\'s performance.

Cross-validation is a technique used to assess a model\\\'s performance by dividing the dataset into multiple subsets, training the model on some, and evaluating it on the remaining data. It helps to obtain a more robust performance estimate.

Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the total actual positives.

The Receiver Operating Characteristic (ROC) curve is a graphical representation of the trade-off between true positive rate and false positive rate at various thresholds. It is often used to evaluate the performance of classification models.

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