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  2. Principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Principal_component_analysis

    Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.

  3. Partial regression plot - Wikipedia

    en.wikipedia.org/wiki/Partial_regression_plot

    The influences of individual data values on the estimation of a coefficient are easy to see in this plot. It is easy to see many kinds of failures of the model or violations of the underlying assumptions (nonlinearity, heteroscedasticity, unusual patterns). . Partial regression plots are related to, but distinct from, partial residual plots.

  4. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  5. Prefix sum - Wikipedia

    en.wikipedia.org/wiki/Prefix_sum

    Prefix sums are trivial to compute in sequential models of computation, by using the formula y i = y i − 1 + x i to compute each output value in sequence order. However, despite their ease of computation, prefix sums are a useful primitive in certain algorithms such as counting sort, [1] [2] and they form the basis of the scan higher-order function in functional programming languages.

  6. Calibration (statistics) - Wikipedia

    en.wikipedia.org/wiki/Calibration_(statistics)

    There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]

  7. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from those of the other observations. High-leverage points , if any, are outliers with respect to the independent variables .

  8. Cross-correlation - Wikipedia

    en.wikipedia.org/wiki/Cross-correlation

    In time series analysis and statistics, the cross-correlation of a pair of random process is the correlation between values of the processes at different times, as a function of the two times. Let ( X t , Y t ) {\displaystyle (X_{t},Y_{t})} be a pair of random processes, and t {\displaystyle t} be any point in time ( t {\displaystyle t} may be ...

  9. Covariance and contravariance (computer science) - Wikipedia

    en.wikipedia.org/wiki/Covariance_and_contra...

    Therefore, both Java and C# treat array types covariantly. For instance, in Java String [] is a subtype of Object [], and in C# string [] is a subtype of object []. As discussed above, covariant arrays lead to problems with writes into the array. Java [4]: 126 and C# deal with this by marking each array object with a type when it is created ...

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