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Machine Learning is a field of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed.
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The three main types of Machine Learning are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
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Supervised learning involves training a model on labeled data, where the desired output is known. Unsupervised learning, on the other hand, deals with unlabeled data and aims to find patterns or structure in the data.
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The curse of dimensionality refers to the challenges that arise when working with high-dimensional data, such as increased computational complexity and the sparsity of data.
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Overfitting occurs when a model learns the training data too well and performs poorly on new, unseen data. It happens when the model becomes too complex and starts to memorize noise or outliers in the training set.
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Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. It helps to control the complexity of the model and encourages simpler models.
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Bias refers to the error introduced by approximating a real-world problem with a simpler model. Variance, on the other hand, measures the model's sensitivity to fluctuations in the training data.
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The ROC (Receiver Operating Characteristic) curve is a graphical representation of the performance of a binary classification model. It plots the true positive rate against the false positive rate at various classification thresholds.
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Cross-validation is a technique used to assess the performance and generalization of a machine learning model. It involves splitting the data into multiple subsets and training/evaluating the model on different combinations of these subsets.
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Feature engineering is the process of selecting, transforming, and creating new features from the raw data to improve the performance of a machine learning model.
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Precision is the ratio of true positive predictions to the total number of positive predictions. Recall is the ratio of true positive predictions to the total number of actual positive instances.
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Bagging (Bootstrap Aggregating) and boosting are ensemble learning techniques. Bagging involves training multiple models on different subsets of the data and combining their predictions. Boosting, on the other hand, focuses on iteratively improving a weak learner by giving more weight to misclassified instances.
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Classification is a task where the goal is to predict the class or category of an instance, while regression involves predicting a continuous numerical value.
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Activation functions introduce non-linearity in neural networks, allowing them to learn complex relationships between inputs and outputs.
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Gradient descent is an optimization algorithm used to minimize the loss or error of a model by iteratively adjusting the model's parameters in the direction of steepest descent.
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A generative model learns the joint probability distribution of the input and output variables, while a discriminative model learns the conditional probability distribution of the output variables given the input.
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L1 regularization adds the absolute values of the coefficients to the loss function, promoting sparsity. L2 regularization adds the squared values of the coefficients, which encourages smaller weights but does not lead to sparsity.
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The bias-variance tradeoff refers to the relationship between a model's bias (error due to assumptions) and variance (error due to sensitivity to training data). A model with high bias tends to underfit the data, while a model with high variance overfits the data.
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Dimensionality reduction is the process of reducing the number of input variables by transforming the data into a lower-dimensional space. It helps to remove irrelevant or redundant features and improve computational efficiency.
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A decision tree is a simple model that makes predictions based on a sequence of binary decisions. A random forest is an ensemble of decision trees, where each tree is trained on a random subset of the data and features.
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Precision measures the proportion of correctly predicted positive instances out of all instances predicted as positive. Accuracy measures the proportion of correctly predicted instances (both positive and negative) out of all instances.
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Kernel methods are algorithms that operate in a high-dimensional feature space by implicitly mapping the data into that space using a kernel function. They enable nonlinear decision boundaries and can handle complex data patterns.
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In online learning, the model learns from individual data instances as they arrive in a sequential manner. In batch learning, the model is trained on the entire dataset at once.
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Regularization adds a penalty term to the loss function, which discourages complex models with high parameter values. It helps to control overfitting by balancing the model's ability to fit the training data and generalize to unseen data.
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Techniques for handling imbalanced datasets include oversampling the minority class, undersampling the majority class, using class weights, and applying advanced sampling techniques like SMOTE (Synthetic Minority Over-sampling Technique).