<?xml version="1.0"?>
<rattle-tooltips>
  <tooltip widget="quit_menu">Quit from the application.</tooltip>
  <tooltip widget="execute_menu">Execute the current tab to have it
    take effect (shortcut is F2).</tooltip>
  <tooltip widget="verbose_menuitem">If enabled, display timestamps in
    plots.</tooltip>
  <tooltip widget="tooltips_menuitem">Enable tooltips under
    GNU/Linux.</tooltip>
  <tooltip widget="ggplot2_menuitem">Experimental: Enable the use of
    the ggplot2 graphs where they have been implemented. Support for
    ggplot2 is on-going and not yet implemented for all plots.</tooltip>
  <tooltip widget="use_cairo_graphics_device">The Cairo graphics
    device is a newer plotting device. Uncheck this to use the default
    R graphics device for your installation.</tooltip>
  <tooltip widget="rattle_menu">Double click to browse to the Rattle
    home page.</tooltip>
  <tooltip widget="execute_button">Execute the current tab to have it
    take effect (shortcut is F2).</tooltip>
  <tooltip widget="new_button">Start a new project.</tooltip>
  <tooltip widget="open_button">Open an existing project from file.</tooltip>
  <tooltip widget="save_button">Save the current project to
    file.</tooltip>
  <tooltip widget="report_toolbutton">Export the information in the
    current tab as an OpenOffice.org report.</tooltip>
  <tooltip widget="export_button">Export the current tab in an
    appropriate form.</tooltip>
  <tooltip widget="stop_button">Attempt to interrupt the current
    process.</tooltip>
  <tooltip widget="quit_button">Quit the application, closing all
    graphics displays and the R console.</tooltip>
  <tooltip widget="connectr_toolbutton">Request a new feature by
  creating a new project for it on the Connect-R crowd sourcing
  service.</tooltip>
  <tooltip widget="data_csv_radiobutton">Load data from a comma
    separated value (CSV) file as might be exported from a
    spreadsheet or database.</tooltip>
  <tooltip widget="data_arff_radiobutton">Load data from an ARFF file
    (CSV with data types) as is used with the Weka data mining
    tool.</tooltip>
  <tooltip widget="data_rdataset_radiobutton">Associate a pre-existing
    R dataset for use.</tooltip>
  <tooltip widget="associate_rules_button">List the rules textually
    in the text view.</tooltip>
  <tooltip widget="associate_plot_button">Plot each rule showing the
    support, confidence and the lift.</tooltip>
  <tooltip widget="data_library_radiobutton">Choose one of the
    datasets supplied by any of the installed R packages.</tooltip>
  <tooltip widget="data_rdata_radiobutton">Load data from a R
    datafile, which is usually a binary file, possibly containing
    multiple datasets.</tooltip>
  <tooltip widget="data_odbc_radiobutton">Identify a data source
    through an ODBC connection.</tooltip>
  <tooltip widget="data_corpus_radiobutton">Load a collection of
    documents for text mining.</tooltip>
  <tooltip widget="data_script_radiobutton">Run a script to generate
    the CSV file to be loaded.</tooltip>
  <tooltip widget="data_filechooserbutton">Open a file chooser to
    select a dataset file to load.</tooltip>
  <tooltip widget="data_separator_entry">The default field separator
    is a comma, but the semicolon (;) is common in Europe where the
    comma is the decimal point. Another common separator is the tab
    (\t) as used in .txt files.</tooltip>
  <tooltip widget="data_decimal_entry">The default decimal point is a
    period, but the comma (,) is common in Europe.</tooltip>
  <tooltip widget="data_header_checkbutton">Indicates whether the
    first row of the datafile is the names of the columns. Otherwise,
    random names will be made up.</tooltip>
  <tooltip widget="data_xdf_checkbutton">XDF is Microsoft's
    PROPRIETARY disk based data format for handling datasets of any
    size, not limited to the available memory. Microsoft's CLOSED SOURCE
    machine learning algorithms can then operate on this data offering
    multiple core (parallel) implementations. When selected any CSV file
    loaded will be transformed to the XDF disk-based format.</tooltip>
  <tooltip widget="data_odbc_dsn_entry">Enter a known ODBC
    data source name (DSN). Then press the Enter key to query the
    database for a list of available tables.</tooltip>
  <tooltip widget="data_odbc_table_combobox">After specifying a DSN a
  list of available tables will appear here. One table is to be chosen
  to be loaded.</tooltip>
  <tooltip widget="data_odbc_limit_spinbutton">Number of rows to
    retrieve from the table.</tooltip>
  <tooltip widget="data_odbc_believeNRows_checkbutton">Some ODBC
  drivers use pre-fetching which confuses the row counts (often by
  returning a fetch limit rather than a row count). If not all rows are
  retrieved after Execution, uncheck this option to not believe the
  reported row count.</tooltip>
  <tooltip widget="data_corpus_location_filechooserbutton">Folder
    containing the corpus and optionally a target.csv file.</tooltip>
  <tooltip widget="data_corpus_strip_checkbutton">Remove extra
    whitespace from the document.</tooltip>
  <tooltip widget="data_corpus_lowercase_checkbutton">Convert all
    words to lowercase.</tooltip>
  <tooltip widget="data_corpus_stopwords_checkbutton">Remove English
    (for now) stop words.</tooltip>
  <tooltip widget="data_corpus_stem_checkbutton">Perform word
    stemming, replacing each word with its stem.</tooltip>
  <tooltip widget="data_sample_entry">Specify how the partitioning is to
  occur. One or two numbers separated by '/' will create a training
  and testing dataset. Three numbers separated by '/' will create a
  training, validation, and testing dataset, in that order. The
  numbers are percentages, and must add up to 100.</tooltip>
  <tooltip widget="data_sample_checkbutton">Randomly partition the whole
    dataset into train/validate/test datasets. Note that the partition is
    created each time this tab is executed, and thus changing the seed
    will generate a different partitioning.
    
    If partitioning is enabled, then any clustering and modelling is
    performed over the training dataset. In the Evaluation tab the
    default dataset for evaluation will be the validate dataset. The
    test dataset is used to obtain an unbiased error
    estimate.</tooltip>
  <tooltip widget="sample_seed_button">Update the seed with a random
    number.</tooltip>
  <tooltip widget="sample_seed_spinbutton">Random number generator
    seed. Change this for different partitions.</tooltip>
  <tooltip widget="variables_set_input_button">To select multiple
    variables for input (perhaps you have previously set them to
    ignore), simply select the required variables in the list below
    and click this Input button.</tooltip>
  <tooltip widget="variables_set_ignore_button">To select multiple
    variables to ignore, simply select the required variables in the
    list below and click this Ignore button.</tooltip>
  <tooltip widget="weight_entry">Enter an R formula for calculating a
    weight for each observation. Variables can be named in the
    formula.</tooltip>
  <tooltip widget="data_target_auto_radiobutton">Determine target type
    automatically as either Categoric or Numeric.</tooltip>
  <tooltip widget="data_target_categoric_radiobutton">Set target type to be
    categoric for Classification.</tooltip>
  <tooltip widget="data_target_numeric_radiobutton">Set target type to be
    numeric for Regression.</tooltip>
  <tooltip widget="data_target_survival_radiobutton">Set target type to be
    suitable for Survival Analysis, requiring both Time and Status
    variables to be identified.</tooltip>
  <tooltip widget="kmeans_plots_label">Once a clustering has been built
  we can display various plots.</tooltip>
  <tooltip widget="summary_radiobutton">Summarise the dataset.</tooltip>
  <tooltip widget="explore_distr_radiobutton">Generate various plots
  to explore the distribution of the data.</tooltip>
  <tooltip widget="explore_interactive_radiobutton">Interactively
    explore the data.</tooltip>
  <tooltip widget="explore_interactive_ggobi_radiobutton">Run the
  external GGobi data visualisation application, if available. The
  GGobi application is independent of R/Rattle and the rggobi package
  provides a connection to it.</tooltip>
  <tooltip widget="explore_interactive_plotbuilder_radiobutton">Run
  the interactive plot builder to generate ggplot2 based plots. The
  Plot Builder uses a Java-based user interface and it has been
  observed to cause R to crash at times.</tooltip>
  <tooltip widget="explore_correlation_radiobutton">Show the pairwise
    correlations between each numeric variable.</tooltip>
  <tooltip widget="prcomp_radiobutton">Perform a principal components
    analysis of the numeric data in the dataset.</tooltip>
  <tooltip widget="explore_sample_label">To turn sampling off, uncheck
    the Partition checkbox in the Data tab.</tooltip>
  <tooltip widget="summary_checkbutton">Summary, including Min,
    Quartiles, Mean, Max for numerics and top levels for
    factors.</tooltip>
  <tooltip widget="describe_checkbutton">Concise description including
    missing, unique, sum, mean, and the lowest and highest
    values.</tooltip>
  <tooltip widget="basics_checkbutton">Various basic measures of
    numeric data, including missing, min, max, quartiles, mean, sum,
    skewness, kurtosis.</tooltip>
  <tooltip widget="kurtosis_checkbutton">Summarise just the kurtosis -
    useful for comparing between all numeric variables at
    once.</tooltip>
  <tooltip widget="skewness_checkbutton">Summarise just the skewness -
    useful for comparing between all numeric variables at
    once.</tooltip>
  <tooltip widget="missing_checkbutton">Generate a table that records
    the pattern of missing values reports the frequency of each
    pattern.</tooltip>
  <tooltip widget="explore_crosstab_checkbutton">Generate cross
    tabulations for each categoric variable with the target
    variable.</tooltip>
  <tooltip widget="explore_correlation_na_checkbutton">Whether we should
    display the correlation between missing values.</tooltip>
  <tooltip widget="benford_bars_checkbutton">Generate a bar plot
    instead of lines.</tooltip>
  <tooltip widget="benford_digits_spinbutton">The digit to plot - 1st
    to 9th are allowed.</tooltip>
  <tooltip widget="benford_abs_radiobutton">Plot the distribution of
    the absolute values.</tooltip>
  <tooltip widget="benford_pos_radiobutton">Plot distribution of only
    the positive values.</tooltip>
  <tooltip widget="benford_neg_radiobutton">Plot distribution of only
    the negative values.</tooltip>
  <tooltip widget="explore_correlation_ordered_checkbutton">Order the
    variables according to strength of correlation.</tooltip>
  <tooltip widget="explore_correlation_method_combobox">Which
    correlation coefficient (or covariance) is to be computed. Pearson
    for bivariate normal distribution. Otherwise Kendall&apos;s tau or
    Spearman&apos;s rho rank based methods may be more
    robust</tooltip>
  <tooltip widget="continuous_clear_button">Clear all check boxes of
    selected rows.</tooltip>
  <tooltip widget="plots_per_page_spinbutton">The number of plots to
    draw per page.</tooltip>
  <tooltip widget="explot_annotate_checkbutton">Include numeric values
    within the plots.</tooltip>
  <tooltip widget="categorical_clear_button">Clear all check boxes of
    selected rows.</tooltip>
  <tooltip widget="label7">Explore the data to identify how it is
    distributed.</tooltip>
  <tooltip widget="test_variance_radiobutton">Two sample t-test to
    test whether the variances are different.</tooltip>
  <tooltip widget="test_kw_radiobutton">Two sample test of means when
    distributions are not known to be normal.</tooltip>
  <tooltip widget="test_ttest_radiobutton">Two sample t-test to test
    whether the means are different.</tooltip>
  <tooltip widget="test_wilcoxon_radiobutton">Two sample Wilcoxon
    rank-sum test, also known as the Mann-Whitney U test, to test
    whether the two medians are different.</tooltip>
  <tooltip widget="test_distr_radiobutton">Test whether the two
    distributions are different.</tooltip>
  <tooltip widget="normalise_radiobutton">Transform numeric data to
    standard scales.</tooltip>
  <tooltip widget="impute_radiobutton">Impute missing
    values.</tooltip>
  <tooltip widget="remap_radiobutton">Remap variables through binning
    or type changes.</tooltip>
  <tooltip widget="cleanup_radiobutton">Delete observations or
    variables.</tooltip>
  <tooltip widget="transform_notebook">Multiply out the selected
    categorics into a new single variable.</tooltip>
  <tooltip widget="rescale_matrix_radiobutton">For the selected
    numeric variables (one or more variables, considered as a matrix)
    divide each cell by the matrix total.</tooltip>
  <tooltip widget="rescale_log_radiobutton">Rescale variables using a
    natural log transform.</tooltip>
  <tooltip widget="rescale_log10_radiobutton">Rescale variables using a
    log10 transform.</tooltip>
  <tooltip widget="normalise_medianad_radiobutton">Rescale (robustly)
    so that the median is 0 and the median absolute deviation is
    1.</tooltip>
  <tooltip widget="normalise_scale01_radiobutton">Rescale to the range
    0-1.</tooltip>
  <tooltip widget="normalise_interval_radiobutton">Rescale to an integer
    in the range 0 to 99 (using the default of 100 groups).</tooltip>
  <tooltip widget="normalise_rank_radiobutton">Change the values into
    a rank order.</tooltip>
  <tooltip widget="normalise_recenter_radiobutton">Rescale so that the
    mean is 0 and the standard deviation is 1.</tooltip>
  <tooltip widget="impute_zero_radiobutton">Replace missing numeric
    with 0 and categoric with 'missing'.</tooltip>
  <tooltip widget="impute_mean_radiobutton">Replace missing values
    with the population mean.</tooltip>
  <tooltip widget="impute_median_radiobutton">Replace missing values
    with the population median.</tooltip>
  <tooltip widget="impute_mode_radiobutton">Replace missing values
    with the population mode.</tooltip>
  <tooltip widget="impute_constant_radiobutton">Replace missing values
    with a specified value.</tooltip>
  <tooltip widget="impute_constant_entry">Specify a value to replace
    missing values with.</tooltip>
  <tooltip widget="label156">Replace missing numeric with 0 and
    categroic with 'missing';.</tooltip>
  <tooltip widget="label14">Partition a numeric variable into a number
    of bins.</tooltip>
  <tooltip widget="remap_quantiles_radiobutton">Each bin will have
  approximately the same number of observations. If the Data tab
  includes a Weight variable then the observations are weighted when
  performing the binning.</tooltip>
  <tooltip widget="remap_kmeans_radiobutton">A kmeans clustering will
    be used to bin the variable.</tooltip>
  <tooltip widget="remap_eqwidth_radiobutton">The min to max range
    will be split into equal width bins.</tooltip>
  <tooltip widget="label15">Set the number of bins to
    construct.</tooltip>
  <tooltip widget="remap_bins_spinbutton">Number of bins.</tooltip>
  <tooltip widget="remap_indicator_radiobutton">Turn a categoric
    into a collection of numeric (0,1) variables.</tooltip>
  <tooltip widget="remap_joincat_radiobutton">Combine multiple
    categoric variables into just one.</tooltip>
  <tooltip widget="delete_ignored_radiobutton">Delete any variables
    that are marked as having role Ignore.</tooltip>
  <tooltip widget="delete_selected_radiobutton">Delete those variables
    selected below.</tooltip>
  <tooltip widget="delete_navars_radiobutton">Delete any variable with
    any missing values.</tooltip>
  <tooltip widget="delete_naents_radiobutton">Delete any observations with
    any missing value.</tooltip>
  <tooltip widget="transform_tab_label">Transform the data in various
    ways.</tooltip>
  <tooltip widget="kmeans_radiobutton">Generate clusters using a
    kmeans algorithm.</tooltip>
  <tooltip widget="ewkm_radiobutton">Generate clusters using a
    kmeans algorithm with subspaces selected by entropy weighting.</tooltip>
  <tooltip widget="hclust_radiobutton">Build an agglomerative
    hierarchical cluster.</tooltip>
  <tooltip widget="biclust_radiobutton">Cluster by identifying
    suitable subsets of both the variables and the
    observations.</tooltip>
  <tooltip widget="kmeans_seed_button">Update the seed with a random
    number.</tooltip>
  <tooltip widget="kmeans_rescale_checkbutton">Automatically re-scale
  the data to be in the range 0-1 to avoid bias toward large valued
  variables.</tooltip>
  <tooltip widget="kmeans_seed_spinbutton">Random number generator
    seed. Change this for different random starting points for the
    clustering.</tooltip>
  <tooltip widget="kmeans_runs_spinbutton">Randomly restart the
    clustering and select the best.</tooltip>
  <tooltip widget="kmeans_hclust_centers_checkbutton">If you have
    performed a hierarchical cluster you can use it to provide initial
    centers for your kmeans cluster.</tooltip>
  <tooltip widget="kmeans_iterate_checkbutton">Iterate over the number
    of clusters, up to the number specified above.</tooltip>
  <tooltip widget="kmeans_stats_button">After a clustering has been
  built generate a list of various measures of the goodness of the
  clustering.</tooltip>
  <tooltip widget="kmeans_data_plot_button">After a clustering has
  been built display a plot of the data and the cluster to which each
  data point belongs.</tooltip>
  <tooltip widget="kmeans_discriminant_plot_button">After a clustering
  has been built display a discriminant plot.</tooltip>
  <tooltip widget="kmeans_weights_plot_button">After an ewkm
  clustering has been built display a plot of the relative weights
  associated with each variable for each cluster, as well as a
  dendrogram showing the correlation between variables and onee
  between clusters.</tooltip>
  <tooltip widget="label30">These options apply when building the
  hierarchy.</tooltip>
  <tooltip widget="label32">These options apply to the constructed
    hierarchy.</tooltip>
  <tooltip widget="hclust_dendrogram_button">Display a dendrogram plot
    of the hierarchical cluster.</tooltip>
  <tooltip widget="associate_baskets_checkbutton">If checked, baskets are
    identified by the ident variable and items in the basket by the
    target variable. Otherwise a basket is the collection of input
    variables.</tooltip>
  <tooltip widget="associate_plot_frequency_button">Plot the frequency
    of the more common items. The support value is used. This includes
    the items that have enough support to appear in the association
    rules.</tooltip>
  <tooltip widget="rpart_radiobutton">Build a model using the popular
    decision tree algorithm.</tooltip>
  <tooltip widget="rf_radiobutton">Build many decision trees using
    random subsets of data and variables.</tooltip>
  <tooltip widget="svm_radiobutton">Build a support vector machine
    model.</tooltip>
  <tooltip widget="model_linear_radiobutton">Build a Linear (numeric
    target) or Logist (binary target) model with lm/glm
    respectively.</tooltip>
  <tooltip widget="nnet_radiobutton">Build a Regression (numeric
    target) or Classification (categoric target) model with
    nnet/multinom respectively.</tooltip>
  <tooltip widget="model_survival_radiobutton">Build a model for
    survival times.</tooltip>
  <tooltip widget="all_models_radiobutton">Build one model of each
    type (except Survival) using the specified parameters.</tooltip>
  <tooltip widget="boost_radiobutton">Build multiple simple binary
    classification models, boosting observations hard to classify,
    combined into an ensemble.</tooltip>
  <tooltip widget="model_tree_priors_label">prior</tooltip>
  <tooltip widget="model_tree_loss_label">loss</tooltip>
  <tooltip widget="label39">minsplit</tooltip>
  <tooltip widget="rpart_minsplit_spinbutton">This is the minimum
    number of observations that must exist in a node resulting from a
    split before a split will be performed. The default is
    20.</tooltip>
  <tooltip widget="model_tree_priors_entry">Set the prior
    probabilities for each class. E.g. for two classes: 0.5,0.5. Must
    add up to 1.</tooltip>
  <tooltip widget="model_tree_loss_entry">Weight the outcome classes
    differently. E.g., 0,10,1,0 (TN, FP, FN, TP).</tooltip>
  <tooltip widget="label84">minbucket</tooltip>
  <tooltip widget="rpart_minbucket_spinbutton">This is the minimum
    number of observations allowed in any leaf node of the decision
    tree. The default value is one thrid of the Min Split.</tooltip>
  <tooltip widget="model_tree_cp_label">cp</tooltip>
  <tooltip widget="label57">maxdepth</tooltip>
  <tooltip widget="model_tree_cp_spinbutton">The complexity parameter
    (cp) is used to control the size of the decision tree and to
    select the optimal tree size. If the cost of adding another
    variable to the decision tree from the current node is above the
    value of cp, then tree building does not continue. We could also
    say that tree construction does not continue unless it would
    decrease the overall lack of fit by a factor of cp.
    
    Setting this to zero will build a tree to its maximum depth (and
    perhaps will build a very, very, large tree). This is useful if
    you want to look at the values for CP for various tree sizes. This
    information will be in the text view window. You will look for the
    number of splits where the sum of the xerror (cross validation
    error, relative to the root node error) and xstd is minimum. This
    is usually early in the list.</tooltip>
  <tooltip widget="rpart_maxdepth_spinbutton">This is the maximum
    depth of any node of the final tree. The root node is considered
    to be depth 0. Note that a depth beyond 30 will give nonsense
    results on 32-bit machines. The default is 30.</tooltip>
  <tooltip widget="rpart_plot_button">Draw the decision tree as a
    plot.</tooltip>
  <tooltip widget="model_tree_include_missing_checkbutton">Check this
    box to use surrogate variables when confronted with missing
    values.</tooltip>
  <tooltip widget="ada_stumps_checkbutton">When active, build
    stumps. Deactivating results in default values.</tooltip>
  <tooltip widget="ada_ntree_spinbutton">The number of trees to
    build. Generally 500 (the default) is good, and the resulting
    model is fairly insensitive to changes here.</tooltip>
  <tooltip widget="ada_maxdepth_spinbutton">This is the maximum
    depth of any node of the final tree. The root node is considered
    to be depth 0. Note that a depth beyond 30 will give nonsense
    results on 32-bit machines. The default is 30.</tooltip>
  <tooltip widget="ada_minsplit_spinbutton">This is the minimum
    number of observations that must exist in a dataset at any node in
    order for a split of that node to be attempted. The default is
    20.</tooltip>
  <tooltip widget="ada_cp_spinbutton">The complexity parameter
    (cp) is used to control the size of the decision tree and to
    select the optimal tree size. If the cost of adding another
    variable to the decision tree from the current node is above the
    value of cp, then tree building does not continue. We could also
    say that tree construction does not continue unless it would
    decrease the overall lack of fit by a factor of cp.
    
    Setting this to zero will build a tree to its maximum depth (and
    perhaps will build a very, very, large tree). This is useful if
    you want to look at the values for CP for various tree sizes. This
    information will be in the text view window. You will look for the
    number of splits where the sum of the xerror (cross validation
    error, relative to the root node error) and xstd is minimum. This
    is usually early in the list.</tooltip>
  <tooltip widget="ada_draw_spinbutton">Which tree to
    display.</tooltip>
  <tooltip widget="ada_list_button">List the decision trees in the
    textview.</tooltip>
  <tooltip widget="ada_errors_button">Plot error rate progressively for
    the number of trees built. Useful for deciding the optimal number
    of trees to build. </tooltip>
  <tooltip widget="ada_importance_button">Show the relative importance
    of variables. Useful to see which variables play the most
    important roles in the model.</tooltip>
  <tooltip widget="ada_defaults_button">Reset the options to
    correspond to the default values.</tooltip>
  <tooltip widget="ada_continue_button">You can build further trees by
    increasing the number of trees to build then click here.</tooltip>
  <tooltip widget="rf_ntree_spinbutton">The number of trees to
    build. Generally 500 (the default) is good, and the resulting
    model is fairly insensitive to changes here.</tooltip>
  <tooltip widget="rf_mtry_spinbutton">The number of variables that
    will be considered at any time in deciding how to partition the
    dataset. For classification the default is the square root of the
    number of variables. The model is generally not very sensitive to
    this value.</tooltip>
  <tooltip widget="model_rf_sample_entry">Specify a single sample size
    or a sample size for each class (e.g., '500,500') which may be
    useful in balancing class predictions.</tooltip>
  <tooltip widget="model_rf_impute_checkbutton">Impute the median
    (numeric) or most frequent (categoric) value for missing data using
    na.roughfix() from randomForest.</tooltip>
  <tooltip widget="rf_importance_checkbutton">Enable this check button
    to display a measure of the importance of each variable. This is
    estimated by considering the increase in prediction error when any
    variable is permuted while all others remain constant. Four
    measures are actually computed, and the resulting plot can be used
    to decide to remove irrelevant variables to achieve model
    reduction (building simpler and more readily interpretable
    models), and to achieve more accurate models.
    
    THIS IS BEING REPLACED BY A Importance Button</tooltip>
  <tooltip widget="rf_proximity_checkbutton">THIS IS BEING REMOVE - IS
    IT NEEDED?</tooltip>
  <tooltip widget="rf_importance_button">Show the relative importance
    of variables. Useful to see which variables play the most
    important roles.</tooltip>
  <tooltip widget="rf_errors_button">Plot error rate prgressively for
    the number of trees built. Useful for deciding the optimal number
    of trees to build.</tooltip>
  <tooltip widget="rf_oob_roc_button">Plot the ROC curve based on the
  out-of-bag (OOB) predictions for each observation in the training
  dataset.</tooltip>
  <tooltip widget="rf_print_tree_button">Display a collection of rules
  for a specific tree or the specific tree from the random forest of
  trees.</tooltip>
  <tooltip widget="rf_print_tree_spinbutton">Which tree to
    display.</tooltip>
  <tooltip widget="kernlab_radiobutton">Use ksvm from the kernlab
    package.</tooltip>
  <tooltip widget="e1071_radiobutton">Use svm from the e1071 package
    (recommended).</tooltip>
  <tooltip widget="svm_classweights_entry">Entry class weights in the
    form: c('0'=4, '1'=1)</tooltip>
  <tooltip widget="model_svm_options_entry">Enter other options passed
    to the ksvm function.</tooltip>
  <tooltip widget="glm_target_label">removed - Makes whole window too
    wide</tooltip>
  <tooltip widget="model_tab_label">Build predictive models.</tooltip>
  <tooltip widget="evaluate_confusion_radiobutton">An error matrix simply lists
    in tabular form the number of predicted cases that are correct
    (true positives and true negatives) and incorrect (false positives
    and false negatives). Both the raw numbers and the proportions
    will be displayed in two tables.</tooltip>
  <tooltip widget="evaluate_risk_radiobutton">A risk chart is used in Analytics
    to plot caseload versus performance data. While traditional plots
    (e.g., ROC Curves) plot false positives against true positives,
    this Risk Chart plots additional information, replacing the x-axis
    with Caseload (the number of cases predicted as hits by the model)
    and the y-axis with Performance. A Risk Variable is identified from
    the dataset. This is a measure of the amount of risk associated
    with each case (e.g., dollars recovered from a fraudulent claim)
    and the plot shows how much of the total risk is recovered for any
    particular caseload. The number of cases recovered (the precision)
    and the strike rate are also shown.</tooltip>
  <tooltip widget="evaluate_lift_radiobutton">A lift chart plots the
    lift against the rate of positive predictions
    (TP+FP)/TOTAL.</tooltip>
  <tooltip widget="evaluate_roc_radiobutton">An ROC curve plots the true
    positive rate against the false positive rate. A better model is
    one with larger area under the curve (better AUC).</tooltip>
  <tooltip widget="evaluate_precision_radiobutton">Plot precision (the positive
    predictive value) against recall (the true positive
    rate).</tooltip>
  <tooltip widget="evaluate_sensitivity_radiobutton">This plots sensitivity
    (the true positive rate, also called recall) against the
    specificity (the true negative rate).</tooltip>
  <tooltip widget="evaluate_score_radiobutton">Score the data and save to
    file.</tooltip>
  <tooltip widget="label132">Choose one or more available models to
    evaluate.</tooltip>
  <tooltip widget="evaluate_training_radiobutton">Evaluate the model
    using the training dataset. This will give an optimistic estimate
    of the performance of the model.</tooltip>
  <tooltip widget="evaluate_validation_radiobutton">Evaluate the model
    using the validation dataset. This is used whilst the model
    parameters are still being tuned but not for the final unbiased
    estimate of error. This option is only available if
    partitioning is enabled in the Data tab and a validation dataset
    is specified.</tooltip>
  <tooltip widget="evaluate_testing_radiobutton">Evaluate the
    performance of the model over the testing dataset, which is the
    remainder of the dataset not used for building, and so will
    provide an unbiased estimate. This option is only available if
    partitioning is enabled in the Data tab and a testing dataset is
    specified.</tooltip>
  <tooltip widget="evaluate_csv_radiobutton">Load a new dataset from a
    CSV file and evaluate the model on this dataset. The dataset must
    have the same columns in the same order as the training
    dataset.</tooltip>
  <tooltip widget="evaluate_rdataset_radiobutton">Use a pre-existing R
    dataset (defined in the R Console) to evaluate the model. The
    dataset must have the same columns in the same order as the
    training dataset.</tooltip>
  <tooltip widget="score_idents_radiobutton">The scores and
    identifiers are written to file.</tooltip>
  <tooltip widget="score_all_radiobutton">The scores and all columns
    are written to file.</tooltip>
  <tooltip widget="evaluate_tab_label">Evaluate the models.</tooltip>
  <tooltip widget="log_tab_label">Show a complete log of all R
    commands. Useful to save as an R script for documentation and
    repeatability.</tooltip>
  <tooltip widget="log_export_comments_checkbutton">When unchecked,
    only the actual R commands will be exported to file, otherwise
    everything we see in the Log is exported.</tooltip>
  <tooltip widget="log_export_rename_checkbutton">Internal variables are
  prefixed with the string 'crs$'. You can choose to use your own
  string to prefix your variables on exporting the Log.</tooltip>
<tooltip widget="log_export_rename_entry">Choose the prefix to be used
  to identify internal variables when this Log is exported.</tooltip>
</rattle-tooltips>
