  We analyze the relationship between the flare X-ray peak flux, and
characteristics of the Polarity Inversion Line (PIL) and Active Regions (AR),
derived from line-of-sight (LOS) magnetograms. The PIL detection algorithm
based on a magnetogram segmentation procedure is applied for each AR with 1
hour cadence. The PIL and AR characteristics are associated with the AR flare
history and divided into flaring and non-flaring cases. Effectiveness of the
derived characteristics for flare forecasting is determined by the number of
non-flaring cases separated from flaring cases by a certain threshold, and by
their Fisher ranking score. The Support Vector Machine (SVM) classifier trained
only on the PIL characteristics is used for the flare prediction. We have
obtained the following results: (1) the PIL characteristics are more effective
than global characteristics of ARs, (2) the highest True Skill Statistics (TSS)
values of 0.76$\pm$0.03 for $\geq$M1.0 flares and 0.84$\pm$0.07 for $\geq$X1.0
flares are obtained using the "Sigmoid" SVM kernel, (3) the TSS scores obtained
using only the LOS magnetograms are slightly lower than the scores obtained
using vector magnetograms, but significantly better than current expert-based
predictions, (4) for prediction of $\geq$M1.0 class flares 74.4 % of all cases,
and 91.2 % for $\geq$X1.0 class, can be pre-classified as negative with no
significant effect on the results, (5) the inclusion of global AR
characteristics does not improve the forecast. The study confirms the unique
role of the PIL region characteristics in the flare initiation process, and
demonstrate possibilities of flare forecasting using only the line-of-sight
magnetograms.
